“This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723906” PROJECT DELIVERABLE REPORT Project Title: Zero-defect manufacturing strategies towards on-line production management for European FACTORies FOF-03-2016 - Zero-defect strategies at system level for multi-stage manufacturing in production lines Deliverable number D1.2 Deliverable title Report on the Analysis of SoA, Existing and Past Projects/Initiatives Submission month of deliverable M2 Issuing partner IRETETH/CERTH Contributing partners CETRI, DATAPIXEL, INOVA+, SIR Dissemination Level (PU/PP/RE/CO): PU Project coordinator Dr. Dionysis Bochtis Tel: +30 24210 96740 Fax:
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“This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723906”
PROJECT DELIVERABLE REPORT
Project Title:
Zero-defect manufacturing strategies towards on-line
production management for European FACTORies FOF-03-2016 - Zero-defect strategies at system level for multi-stage manufacturing in
production lines
Deliverable number D1.2
Deliverable title Report on the Analysis of SoA, Existing and
Past Projects/Initiatives
Submission month of deliverable M2
Issuing partner IRETETH/CERTH
Contributing partners CETRI, DATAPIXEL, INOVA+, SIR
2 State of the Art .................................................................................................................................................... 2
2.2 Early Malfunction detection and analysis .............................................................................................. 4
2.3 Real time optimization .............................................................................................................................. 7
2.4 Green scheduling ....................................................................................................................................... 9
2.5 Laser scanning and 3D vision ................................................................................................................ 10
3 Past and Ongoing Projects .............................................................................................................................. 33
3.1 Past Projects ............................................................................................................................................. 33
This report focuses on the State of the Art, regarding Zero Defect Manufacturing Technologies and their
implementation in past and present projects. The State of the Art of some of the technologies that will be
utilized in Z-fact0r are presented in paragraph 2. In paragraph 3, a detailed list of these projects is presented.
For each project, various information is included such as a brief summary, consortia and budget.
Additionally, initiatives that promote Zero Defect Manufacturing and facilitate communication and
dissemination are also presented in paragraph 3.
In paragraph 4, we extensively present the current Trends in Manufacturing Intelligence and in paragraph
5 we present the SWOT analysis where the possible opportunities and possible threats are presented in
detail.
Finally, we conclude with the results of our research in in past and present projects, as well as the
technologies that emerged in paragraph 6.
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2 State of the Art
The following technologies have been considered and explored in order to better understand what is the
current State of the Art in each one. These technologies play crucial role in the Z-fact0r as a whole, and
will eventually be implemented in the final integrated solution. The technologies are mentioned and
described in the following paragraphs.
2.1 DSS
The improvement of quality targets and zero-defect management in manufacturing processes necessitates
the development of new and emerging technologies1. In this context, the implementation of the production
quality paradigm requires advanced technologies for on-line data gathering, incorporating:
(i) 3D flexible part verification through integration of multi-sensor, multi-resolution systems; and
(ii) ICT architectures to support in-line inspection and data sharing at system level.
In this regard, technologies are required to provide higher degree of machine-condition awareness and
advanced diagnostics and maintenance ability with lower interference with the system production rate. For
this purpose, ICT-based decision support systems can support the production quality principle by vertically
transferring process, quality control and diagnostics data at decision making level and vice versa, by
achieving interoperability and integration of multi-scale and heterogeneous shop floor data and by avoiding
defect generation in manual operations through virtual and augmented reality tools. Besides, an ontology-
based framework can be used to share consistent design and shop floor data between different
heterogeneous software tools including, 3D virtual environments, discrete event simulation models and
analytical models, at both process and system levels2.
A Decision Support System (DSS) is a computer-based information system that supports organizational
decision making activities. The objectives of a DSS is to produce decisions for a problem by analysing the
data, in an intelligent and fast way that a human cannot perform in reasonable time, resulting in more
informed decisions3.
There are many aspects of the manufacturing systems that need the support of DSS4. The most important
aspects that are frequently addressed in the literature are:
1) the control of the manufacturing line,
2) the supply chain management,
3) the quality control,
4) the information recording and
5) self-learning production system.
With respect to the control of the manufacturing line, Guo et al.5 formulated the problem as a minimization
of an objective function, so as to satisfy the desired cycle time of each order and to minimize the total idle
1 Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. CIRP Annals - Manufacturing Technology, 63(2), 773–796 2 Tsung F, Li Y, Jin M (2008) Statistical Process Control for Multistage Manufacturing and Service Operations: A Review and Some Extensions. International Journal of Services Operations and Informatics 3(2):191–204 3 Schmitt, R., Monostori, L., Glöckner, H., & Viharos, Z. J. (2012). Design and assessment of quality control loops for stable business processes. CIRP Annals - Manufacturing Technology, 61(1), 439–444. D. Arnott, G. Pervan, Eight key issues for the decision support systems discipline, Decision Support Systems 44 (3) (2008) 657–672 4 Shibl, Rania, Meredith Lawley, and Justin Debuse. "Factors influencing decision support system acceptance." Decision Support Systems 54.2 (2013): 953-961 5 Guo, Z. X., et al. "Intelligent production control decision support system for flexible assembly lines." Expert Systems with Applications 36.3 (2009): 4268-4277.
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time of all workstations. In the same line, Mok6 proposed an evolutionary optimization methodology in
order to provide both crisp-logic and fuzzy-logic production control for an assembly line. With respect to
supply chain management, Dong et al.7 proposed a simulation based optimization procedure in order to
support the replenishment of materials in a production line. Mansouri et al.8 proposed a method based on
multi-objective optimization for build-to-order supply chain management. As far as quality control is
concerned, Gong and Chen9 utilized eleven indices to denote the manufacturing performance of fabrics,
which involve different processes of apparel manufacturing, such as laying, cutting, overall handling, and
interplay shifting.
Yanmei et al.10 propose the use of four indexes, namely, average displacement, shrinkage rate of seam, seam
pucker and straight degree of seam, in order to evaluate the sewing quality of fabrics in a production line.
These indexes are afterwards fed into a fuzzy recognition model that can effectively evaluate and quantify
the sewing quality. Although the aforementioned methods are used for evaluating the quality of the
products in a production line, it is also import to predict the product quality in the future time periods, so
as to follow proactive actions before the quality deteriorates.
Towards this end, Hui et al.11 use logarithm regression and artificial neural networks in order to predict the
seam performance of commercial woven fabrics based on 12 fabric properties. With respect to information
recording, Putnik et al. proposed the use of smart objects that incorporate quality management functions
to accurately support decision- making processes. Each smart object is able to communicate effectively
with its environment and collect and store information. The smart objects can correspond to production
products or machines in the assembly line. On a similar manner, Zhuming et al. proposed the use of Internet
of Things, in order to have functionality similar to the aforementioned smart objects. As for self-learning
production system, Giovanni et al. tries to enhance the control along with other manufacturing activities.
Despite the apparent growth of the decision support systems in the manufacturing field, there are still some
issues that have not been considered for the efficient design of DSSs. The most important issue is that
previous approaches focused on the decision support of specific aspects of the production procedure, i.e.
control of the manufacturing line, supply chain management, quality control, and information recording.
However, these different aspects of the production procedure are not independent of each other, and some
aspects of them can be organized in a hierarchical manner, e.g. low quality in the product might indicate an
issue in the control of the manufacturing line. Although hierarchical DSSs have been suggested in other
fields, their application in manufacturing is limited.
In monitoring and control activities for modern manufacturing systems, the role of cognitive computing
methods employed in the implementation of intelligent sensors and sensorial systems is a fundamental one.
For this purpose, several schemes, techniques and paradigms have been used to develop decision making
support systems functional to come to a conclusion on machining process conditions based on sensor
signals data features12. The cognitive paradigms most frequently employed for the purpose of sensor
6 Mok, P. Y. (2009). A decision support system for the production control of a semiconductor packaging assembly line. Expert Systems with Applications, 36(3), 4423-4430. 7 Dong, A. H., & Leung, S. Y. S. (2009). A simulation-based replenishment model for the textile industry. Textile Research Journal, 79(13), 1188-1201. 8 Mansouri, S. A., Gallear, D., & Askariazad, M. H. (2012). Decision support for build-to-order supply chain management through multiobjective optimization. International Journal of Production Economics, 135(1), 24-36. 9 Gong, R., & Chen, Y. (1999). Predicting the performance of fabrics in garment manufacturing with artificial neural networks. Textile research journal, 69(7), 477-482. 10 Li, Yanmei., & Zhang, W. (2008, October). Evaluation of sewing quality in appearance based on fuzzy recognition. In Fifth International Conference on Fuzzy Systems and Knowledge Discovery (pp. 164-167). IEEE. 11 Hui C and Ng S. Predicting seam performance of commercial woven fabrics using multiple logarithm regression and artificial neural networks. Textil Res J 2009; 79: 1649–1657. 12 Alves, C., & Shah, V. (2015). Smart objects embedded production and quality management functions.
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monitoring in machining, include neural networks, fuzzy logic, genetic algorithms and hybrid systems which
are able to combine the capabilities of the various cognitive methods.
Intelligent data analysis and classification methodologies have been proposed in the last years in order to
predict the behavior of machines and processes and to provide fault diagnosis based on predictor
variables13. Thus, taking into account the results and the conclusions achieved from such methods,
knowledge extraction and decision making support tasks can be accomplished with the aim of reinforcing
holistic quality system and suggest actions to be performed in order to maintain the resources in the system.
There are several recent techniques that deal with this issue. The most important include Decision and
Manufacturing and service processes today usually involve more than one process stages and operations.
With an emphasis on achieving satisfactory product and service quality, multistage processes surveillance
and fault diagnosis has become a necessity14. Statistical Process Control (SPC) methods have been widely
recognised as effective approaches for process monitoring and diagnosis. However, most conventional SPC
methods focus on single-stage processes without considering the multistage scenario.
A decision support system for visualizing and managing interactions among different quality and process
control loops at shop floor level has been developed in a recent study by Schmitt et al.15. The concept of
the software combines the assessment of a quality control loop and its step-by-step improvement, and the
tool can additionally be used to efficiently guide the user through all the steps that are required for defining
a new quality control loop to be implemented. Besides, another recent study developed and applied a similar
software tool that enables to vertically connect maintenance data to production and business data for
improved decision making. Furthermore, recently knowledge-based virtual platforms have been developed
for enabling data exchange and interoperability among heterogeneous ICT tools for factory planning,
reconfiguration and management.
2.2 Early Malfunction detection and analysis
The field of fault detection in process industry is divided in two main categories: data-driven methods and
model-based methods. Data driven-fault detection methods use only historical data from several plant or
industrial processes. The data driven-approach requires no prior knowledge about those specific processes.
These methods are trained mainly with no-fault data, and used to recognize deviations of the specific
process from normal behavior. On the other hand, model-based fault detection methods develop the
model(s) of a specified industrial or plant process and compare the model output with the one of the real
process, at the same time (model process-time and real process-time) and under the same process
parameters.
The most common approach to data-driven fault detection is to treat the problem as a supervised learning
one and apply related algorithms to data that is either collected from real plant processes or synthesized
datasets created from simulations. Different variations on this theme can be found in the literature, which
Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of Things for enterprise systems of modern manufacturing. Industrial Informatics, IEEE Transactions on, 10(2), 1537-1546 13 Orio G. D., Candilo G., Barata J., The Adapter module: A building block for Self-Learning Production System. Robotics and Computer – Integrated Manufacturing 26 (2015) 25-35 14 Gören, H. G., & Kulak, O. (2014). A new fuzzy multi-criteria decision making approach: Extended hierarchical fuzzy axiomatic design approach with risk factors. In Decision Support Systems III-Impact of Decision Support Systems for Global Environments (pp. 141-156). Springer International Publishing. 15 Schmitt, R., Monostori, L., Glöckner, H., & Viharos, Z. J. (2012). Design and assessment of quality control loops for stable business processes. CIRP Annals - Manufacturing Technology, 61(1), 439–444.
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typically depend on the data and the particular plant process the authors are trying to model. In Igor Santos
et al16 the authors are tackling the problem of microshrinkages (porosities found when the cast is cooling
down) appearing during foundry production by modeling the foundry process. Their method involves
simple supervised training with 10-fold cross validation of different models on real world annotated data
collected from the relevant industry. The predicted variable is classified according to risk levels associated
with defect occurrence probability. The best performing techniques were Bayesian Networks along with
Artificial Neural Networks (3-layer feedforward network with 15 units in the hidden layer, trained with
backpropagation) achieving ~85% accuracy, optimized according to Root Mean Squared Error and Mean
Absolute Error functions. Varghese et al.17 train 4 different algorithms on real-world data for classifying
errors, namely linear regression, logistic regression, CART (Classification And Regression Trees) and neural
networks. All perform adequately with achieved accuracy close to 99%, with the top two being CART and
neural networks achieving 99.57% and 99.35% respectively. It should be noted however that the authors
do not make clear what kind of process they model nor do they elaborate on the details of the data set on
which they train their algorithms. Xiao18 trains his models on data from the NASA Ames Prognostic Data
Repository, which consists of three data sets (training, validation and test set) containing time series data
from 33, 15 and 15 plants respectively. The data sets contain readings from 4 sensors and 4 reference
signals, time series data for cumulative energy consumed and instantaneous power from a fixed number of
zones within a given plant and finally the plant fault events characterized by a start time, end time and fault
type. The test set contains complete fault event data in its first half but the data for the latter half has been
randomly removed. Xiao’s18 objective is to predict the deleted events, in terms of start time, end time and
fault type. The time signature prediction makes this body of work very important as the learned models
predict when a fault will occur (as well as when it will end) along with its type. His method is multi-stage,
first predicting an event start time which yields a set of probabilities for start times. Based on this, he solves
another classification problem of predicting the event’s end time. Various algorithms were used but the
author decided to base his final approach on gradient boosting machine (GBM), random forest, and logistic
regression with L2 regularization. On the first phase of his approach (predict start time), Xiao found that
an ensemble of logistic regression and random forest. On the second phase (predict end time), the best
performing algorithm was GBM with tree number set to 200 and tree depth set to 5. It is noted that the
author performed considerable feature engineering.
Moving beyond the supervised learning framework, Grbovic et al19 argue for a fusion of supervised and
unsupervised techniques. Their work is motivated by the fact that the supervised learning assumption that
a given dataset contains all fault types is often violated in real-world applications. Furthermore, they propose
a mix of supervised and unsupervised methods as unsupervised models have the capacity to detect both
unknown faults and known ones, whereas supervised models have higher accuracy on known faults but
cannot detect unknown ones. Their proposed method is as follows: Unsupervised models (PCA) are
trained on normal operation data. Upon arrival of fault data (in batches) a supervised model (SVM) is
trained and a heuristic rule is selected for the fusion of two models. Grbovic et al. call this latter part an
“incremental update” to the system. This process is repeated for all subsequent batches of fault data. The
authors’ approach adapts to both supervised (fully-annotated new data) and semi-supervised (partially-
16 Igor Santos et al., Optimising machine-Learning-based fault prediction in foundry production, in Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berl in, Heidelber, 2009 17 Linda Varghese et al., Data driven approach to monitoring and fault detection in process control plants, in IJCTA, 8(3), 2015 18 Wei Xiao, A probabilistic machine learning approach to detect industrial plant faults, in International Journal of Prognostics and Health Management 7(1):11, March 2016 19 Mihajlo Grbovic et al., Cold start approach for data-driven fault detection, in IEEE Transactions on Industrial Informatics, Vol. 9, No. 4, November 2013
annotated new data) scenarios. The performance of their approach is superior to purely supervised or
unsupervised methods on the “Tennessee Eastman Process” synthetic data set.
An interesting data-driven approach to traditional plant process control is adopted in Chang and Chang20
and Zhirabok and Pavlov21 for model-free fault detection in industrial processes. Chang and Chang’s
approach is two-step: first they calculate the critical parameters of the plant’s transient state and steady state
offline. Then they implement those online for process monitoring. The authors consider a second-order
system which can generalize to higher-order systems and estimate the settling time threshold as well as the
steady-state control limits offline, using sensor sampled data and then they implement the system’s transfer
function using the settling time estimate. Chang and Chang’s method correctly detects problems in the
transient state (early detection) and the steady state. Zhirabok and Pavlov aim for fault detection in non-
linear systems, where the non-linearities are non-smooth (e.g. industrial/technical systems with
disturbances like saturation, Coulomb friction, backlash and hysteresis). They also aim to decrease
computational complexity where this is possible. Their method involves transforming the system (described
by state, control and output vectors) so as to describe it in new coordinates. The resulting expression is
written in matrix form. This matrix is decomposed using singular value decomposition (SVD), then the
vector that allows one to determine whether the system is “healthy” is a linear combination of the rows of
the Σ matrix.
Finally, a novel approach was adopted by Vafeiadis et al.2223 where they address the problem of online
incident (fault) detection on 2 interdependent time series by modeling time series data, using modified
versions of SSP (Slope Statistic Profile). SSP estimates “the change point (or breakpoint T) from the profile
of a linear trend test statistic, computed on consecutive overlapping time windows along the time series”22.
The breakpoint T is investigated in intervals defined by bands for positive and negative trends. These
boundaries of these are contingent on confidence levels which model the transition from one state (e.g.
normal operation to fault) to another. In their first modification, RTSSP (Real Time SSP), parametric linear
trend tests give rise to the curves for the two-time series. The tests are performed at each time interval of
the window slide. An important extension to the SSP is the adjustment of window size according to real
time classification on the basis of two different linear trend scenarios. On the first, the controller output
lies between no trend and negative trend and the expectation of temperature moves is between no trend
and positive trend. On the second scenario this situation is reversed. The size of the window is recalculated
every 100 time steps according to classification F scores obtained in real-time. RT-SSP provides real-time
estimations of possible changes (i.e. incidents) each time one of the two curves crosses the threshold line
of rejection of the null hypothesis of no trend into the upper/lower segment. The second modification to
the SSP, called MSSP (Modified SSP) differs from RT-SSP only in that it uses a different trend test statistic
and that it does away with bands to describe the change of trend but thresholds are defined by single points
for the positive and negative trends respectively.
20 Allen Chang and Yaw-Jen Chang, Data-Driven Approach of Fault Detection for Customized Manufacturing, in Proceedings of the International MultiConference of Engineers and Computer Scientists 2016 Vol I, IMECS 2016, Hong Kong, March 16 - 18, 2016 21 Alexey Zhirabok and Sergey Pavlov, Data-driven method of fault detection in technical systems, in 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2014 22 T. Vafeiadis et al., Real-time incident detection: an approach for two interdependent time series, in European Signal Processing Conference (EUSIPCO’16), Budapest, Hungary, 29 August – 02 September 2016 23 T. Vafeiadis et al., Robust malfunction diagnosis in process industry time series, in 14th IEEE International Conference on Industrial Informatics (INDIN’16), Futurescope, Poitiers, France, 18-21 July 2016.
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For the model-based approach to fault detection Dragan24 bases his method on a two-stage approach prior
knowledge and recorded data are combined. In the first stage, the initial model is built from reasoning based
on first principles. Prior assumptions, the available instrumentation and the diagnostic requirements
complete the modeling procedure resulting in a linear continuous-time model. The author’s work is flexible
on integrating prior assumptions with the modeling procedure as it accounts for the quality of those
assumptions. If prior knowledge is poor then the description of causal relationship between process
variables is defined qualitatively by sign, interval or fuzzy sets. In the case of perfect prior knowledge these
relationships are described by precise expressions defined on the set of real numbers. Further model
parameters are estimated using the method of least squares which results in estimates that are invariant of
the bandwidth of the SVF (State Variable Filters), which are used to model the plant process. Fault
identification is then a simple matter of on-line calculation of the residual signal, i.e. the difference between
the process output and the predicted output. Large differences indicate the presence of a fault, while small
ones indicate normal operation conditions. Nozari et al.25 propose a method for robust fault detection and
isolation (RFDI) for a gas turbine prototype. Their method is composed of two parts: first fault detection
is based on non-linear modeling of normal behavior derived from plant process monitoring with the help
of an MLP (aka neural network). Error modeling then is achieved through local linear neuro fuzzy (LLNF)
models. A level of uncertainty is also modeled with nominal system output and the LLNF output being
added with the final result being the generation of upper and lower bounds for the error. Finally, error/fault
detection is based on the tracking of the residual signal of the process. Once the signal deflects from the
preset thresholds, an error is considered to have been observed. The second part of Nozari et al.’s method
is fault isolation which is achieved by a 2-layer MLP classifier that makes decisions on the relations between
symptoms and faults. Of note is the fact the for the MLP fault predictor is that the model “contains past
values of inputs and output that are generated by injecting the current inputs and output of the system into
a bank of TDL filters”25. Finally Wang et al.26 consider a hidden Markov model (HMM) where two global
probabilistic indicators are calculated for error detection, one for each mode of operation (steady and
transitional). The indicators are based on the negative log likelihood probability.
There is a wealth of methodologies for attacking event detection in plant processes and machine and
statistical learning is an integral part of most, however one should always keep in mind that these algorithms’
performance is often dependent on the problem they are trying to model, as well as the data set used to
train them. Model-based approaches that do not rely extensively to data-driven methods are unaffected by
this, but the accuracy of the model can vary based on how accurately disturbances to the process can be
accounted for and modeled.
2.3 Real time optimization
Real-time optimization (RTO) of On-Line Optimization (OLO) is a non-stoppable process and continues
re-evaluation and re-calculation of operating condition (parameters) of a process model so that the
performance of the specified process to be maximized. Optimization of a process operation is of increasing
interest in industry 4.0 due to increasing global competition and more strict and demanding product
requirements.
24 Dejan Dragan, Fault detection of an industrial heat-exchanger: a model based approach, in Strojniški vestnik - Journal of Mechanical Engineering, 2010 25 Hasan A. Nozari et al., Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques, in Neurocomputing, Vol.91, 15 August 2012. 26 Fan Wang et al., Hidden markov model-based fault detection approach for a multimode process, in Ind. Eng. Chem. Res., 2016.
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Hanmandlu et al.27 suggested that a general real-time optimization structure is based on specific steps. The
steps are given below:
Steady-state detection
Data reconciliation and gross error detection
Parameter estimation
Process model
Optimization
Updating process set points
The optimal operating conditions for a plant are determined as part of the process design. That would
normally fall into the modeling of the process. However, during plant operations, due to changes in
equipment availability, economic conditions, and process disturbances, the optimal conditions change
frequently over the course of time. Hence, the optimal operating conditions need to be re-calculated on a
regular basis. This control activity is defined as real-time optimization. RTO utilizes the plant operating
conditions for variables to predict properties such as product characteristics. A suitable problem statement
needs to be formulated and solved once a process has been selected. For RTO, optimization of set points
requires two elements - the operating model and an objective function (also called cost or loss function) that
evaluates the model’s operation. The operating model is a steady-state process model and contains all
process variable constraints. The objective function could for instance include costs and/or product values.
Depending on its form, the objective function needs to be maximized or minimized.
In the literature, one can find several approaches and methods for real-time optimization. These methods
are:
Global or Centralized Approach to Real-Time Optimization
Distributed Approach for Real-Time Optimization
Direct Methods (Model free approaches)
Global or Centralized Approach to Real-Time Optimization considers the plant as general ontology. Thus,
the plant is optimized as one using only one objective function, taking into account its internal constraints,
and a model of the entire plant. The Distributed Approach for Real-Time Optimization splits the overall
plant optimization into several, dependent or independent, optimization functions, which are coordinated
by a single optimization - coordination model. In this specific case, the objective function is a part of a
multi-level control structure. The distributed approach is also sometimes referred to as modular or
hierarchical optimization. Finally, at Direct Method approach, the real-time optimization is performed
directly on the process without using any model. Global or Centralized Approach and Distributed
Approach are the most common approaches one can find in scientific literature and industrial applications.
Following, several state-of-art real-time optimization techniques and methodologies are described. The
most common approach for researches to address the problem of real-time optimization in process industry
27 Hanmandlu, M., Purkayastha, P., Pal, J.K. (1985). On the Use of Nonlinear Programming in Real Time Control in Process Industries. IFAC Control Applications of Nonlinear Programming and Optimization Conference, Capri, Italy, Pergamon Press, Oxford, U.K., pp71-78
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is the use of Nonlinear Model Predictive Control (NMPC) governed by differential algebraic equations2829.
Cubillos et al.30 applies grey-box neural models (GNM) on the Williams-Otto reactor. These models are
based on a suitable combination of fundamental conservation laws and neural networks, being used in at
least two different ways: to complement available phenomenological knowledge with empirical information,
or to reduce dimensionality of complex rigorous physical models. Another framework for defining general
control policies is Reinforcement Learning (RL). Reinforcement Learning algorithms are agent-based and
in general they formulate the problem in terms of Markov Decision Processes (MDPs), where the agent
aims to maximize the reward over a series of state transitions (captured in terms of the state value function
V or action value function Q). In terms of process control, the objective function to be maximized is the
state/action value function. There are model-based and model-free and recently even hybrid algorithms
within the RL literature but on-line learning (aka optimization) in RL involves solving problems where the
goal consists of either:
- Minimizing regret, i.e., the difference of the total reward that would have been achieved by the
optimal policy and that received by the learner, or
- Minimizing the number of time steps when the algorithm’s future expected return falls short of
the optimal expected return by some pre-specified amount.
For the first problem one can employ the UCRL2 algorithm, while for the second, the PAC-MDP (Delayed
Q-Learning31, MBIE32, R-max3334) family of algorithms is best suited.
2.4 Green scheduling
Green scheduling is a novel concept that first coined by Mansouri et al.35. It extends the current practice of
conventional scheduling by explicitly considering energy consumption and emissions as performance
indicators in manufacturing scheduling. Manufacturing scheduling has traditionally been performed by
performance- and cost-oriented metrics such as makespan, float time, tardiness, and staff cost.
Minimizing carbon emissions on the shop floor involves multifaceted challenges that necessitate a multi-
objective approach because of conflicting objectives of, for example, makespan and energy consumption.
It entails complex decision making and trade-off analysis by the operations managers. As one of the first
attempts in this field, Mansouri et al.35 addressed a bi-criteria two-machine flowshop scheduling problem
to minimize total energy consumption and makespan. They showed the conflict between the two
28 M. Diehla, H.G. Bocka, J. P. Schlödera, R. Findeisenb, Z. Nagyc, F. Allgöwerb (2002). Real time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations, Journal of Process Control, vol. 12 (4), 2002, pp. 577-585 29 M. Diehl, I. Uslu, R. Findeisen, S. Schwarzkopf, F. Allgower, H.G. Bock, T. Burner, E.D. Gilles, A. Kienle, J.P. Schloder, E. Stein, Real-Time Optimization for Large Scale Processes: Nonlinear Model Predictive Control of a High Purity Distillation Column, Online Optimization of Large Scale Systems, 2001, pp.363-383 30 F.A. Cubillos, G. Acuna, E.L. Lima, Real-time process optimization based on grey-box neural models, Brazilian Journal of Chemical Engineering, vol. 24, no. 3 2007 31 Strehl, A. L., Li, L., Wiewiora, E., Langford, J., and Littman, M. L. (2006). PAC model-free reinforcement learning. In Cohen and Moore (2006), pages 881–888 32 Strehl, A. L. and Littman, M. L. (2005). A theoretical analysis of model-based interval estimation. In De Raedt and Wrobel (2005), pages 857–864 33 Kakade, S. (2003). On the sample complexity of reinforcement learning. PhD thesis, Gatsby Computational Neuroscience Unit, University College London. 34 Brafman, R. I. and Tennenholtz, M. (2002). R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 3:213– 231 35 Mansouri, S.A., Aktas, E. and Besikci, U., 2016. Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption. European Journal of Operational Research, 248(3), 772-788
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objectives and developed an O(n3) heuristic to solve large size problems. Mansouri and Aktas36 extended
this work and developed a new O(n2) heuristic and multi-objective genetic algorithms (MOGAs) for a
sequence-dependent two-machine permutation flowshop scheduling problem to extend applicability of
the concept of green scheduling in real life applications. In particular, they examined the effect of
hybridizing the MOGA with the constructive heuristics to improve the efficiency and the effectiveness
of the search. The authors validated the performance of the developed solution techniques through
comprehensive experiments based on three performance metrics, namely, quality (distance with the lower
bound to the problem), diversity (number of unique sequences in the solution set), and cardinality (size
of the solution set) of the Pareto frontiers. The literature on green scheduling is still in its infancy stage
and is expected to evolve in coming years due to growing importance of environmental sustainability of
manufacturing operations. A non-inclusive list of recent developments on green scheduling is reported
by Liu et al.37, Wang et al.38 and Zheng and Wang39.
2.5 Laser scanning and 3D vision
Industrial inspection and metrology anxiously awaits advances in 3D vision technology. Here the
overlap in requirements with high volume applications is smaller—inspection and metrology typically
require information on a sub-millimeter length scale (and in some cases down the sub-micron level).
Structured light will likely be the preferred technology; however, development is still required to push the
boundaries of spatial accuracy and speed of acquisition40.
An example can be a new tool developed by Purdue University to detect flaws in lithium-ion batteries41 as
they are being manufactured, a step toward reducing defects and inconsistencies in the thickness of
electrodes that affect battery life and reliability.
In Automotive manufacturing the errors imply high costs so vision systems have become the key to
ensuring high levels of quality and minimizing warranty costs. Different vision inspection systems are
integrated like eFlex Vision with Cognex Cameras42
There are many companies dedicated to 3D inspection software and electronic devices for the
manufacturing quality controls developing constantly new products and software to incorporate the new
technological advances. Examples of them are Metrologic group, Datapixel S.L., Mahr Federal, ZEISS,
Kreon, Exact Metrology, Hexagon43 or Perceptron are some examples.
36 Mansouri, S.A. and Aktas, E., 2016. Minimizing energy consumption and makespan in a two-machine flowshop scheduling problem. Journal of the Operational Research Society, 67 (11). 1382 - 1394 37 Liu, Y., Dong, H., Lohse, N. and Petrovic, S., 2016. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, 259-272 38 Wang, S., Liu, M., Chu, F. and Chu, C., 2016. Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration. Journal of Cleaner Production, 137, 1205-1215 39 Zheng, X.L. and Wang, L., 2016. A Collaborative Multiobjective Fruit Fly Optimization Algorithm for the Resource Constrained Unrelated Parallel Machine Green Scheduling Problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems (forthcoming) 40 CMOS Sensors Continue to Advance, Qualitymag, 2016 [Online] Available: http://www.qualitymag.com/articles/93707-cmos-sensors-continue-to-advance 41New technique to improve quality control of lithium-ion bateries, Purdue University 2013. [Online]. Available: http://www.purdue.edu/newsroom/releases/2013/Q2/new-technique-to-improve-quality-control-of-lithium-ion-batteries.html 42 eFlex SYSTEMS, 2016. [Online]. Available: http://www.eflexsystems.com/download-eflex-vision-case-study-cognex 43 Hexagon, 2016 [Online] Available: http://www.hexagonmi.com/products/coordinate-measuring-machines/shop-floor-cmms
From Rockwell Software, FactoryTalk® View Machine Edition (ME)61 software is a versatile HMI
application that provides a dedicated and powerful solution for machine-level operator interface devices.
An integral element which provides superior graphics, run-time user management, language switching and
faster commissioning time through a common development environment. RSView®32™62 is an
integrated, component-based HMI for monitoring and controlling automation machines and processes
providing unprecedented connectivity to other Rockwell Software products, Microsoft products, and third-
party applications.
ICONICS embraces the open-communication standards, OPC and OPC UA, solution for
intercommunicating different hardware and software. ICONICS solution for SCADA provides a complete
view of the operation, including sparkling 3D graphics, trending, reporting and alarming. The ICONICS
solution is scalable, it has been installed on customer applications, from just a few hundred monitored
points, to several million points, monitored from a single location.
2.7 Programmable Logic Controllers
Early PLCs' only function was to manipulate relays wired to its discrete inputs63. Thirty years later, they
remain a steadfast and growing component to various industries. PLCs fit the bill of replacing single-loop
controllers. They provide flexibility to do all data acquisition, assist the DCS without high cost, are popular
within other industries which, gives allowance to a greater familiarity among customers. Along with
flexibility, speed has become another important development in PLC and I/O technology. The industry
first delivered Ethernet communication modules for large, expensive PLCs. But more recently, less
expensive 10Base-T and 10Base-F PLC Ethernet modules have become available, along with Ethernet slave
modules for field I/O nodes in PC controls and hybrid systems. Future plans for programmable logic
controllers include PLCs with more remote I/O modules, PLCs with more plug-in modules, PLC I/O
networked to PCs, and more micro PLCs. The future of PLCs seems bright, and it is still not clear whether
or not PC-based solutions will dominate future industrial control. PC-based controls will play an important
role until PLCs and PLC-type systems become more open, intelligent, affordable, and move away from
proprietary networks. Users demand the kind of flexibility that PC-based systems launched, the ability to
change software layers without changing hardware, and to use commercially available technology whenever
appropriate or possible. There was an increase in PC-control systems over the past years. Technology is
getting easier to apply and therefore is getting increased acceptance.
2.7.1 Programmable Logic Controller Products
The global PLC market is highly competitive due to the presence of numerous tier I and tier II players that
have the capability to acquire high-value automation solution projects from end users. In terms of PLC
solutions, the market participants are making every effort to address requirements in the areas of price,
quality, durability, scalability, interoperability, service, and distribution. The vendors in this market are
extending their support by introducing end-user-specific PLC solutions to strengthen their market foothold.
Leading vendors in the market are:
Mitsubishi
Rockwell
Schneider
Siemens
61 http://www.rockwellautomation.com/rockwellsoftware/products/factorytalk-view-me.page 62 http://www.rockwellautomation.com/rockwellsoftware/products/rsview32.page 63 CE , Oct. 1995, 'The Evolution of PLC-Based Loop Control,' p. 57.)
72 X. Jia, O. Feng, T. Fan, and Q. Lei, “RFID technology and its applications in Internet of Things (IoT),” in Proceedings of the 2nd IEEE International Conference on Consumer Electronics, Communications and Networks (CECNet), April 21-23, 2012, pp.1282-1285. 73 M. K. Lim, W. Bahr, and S. Leung, “RFID in the warehouse: a literature analysis (1995–2010) of its applications, benefits, challenges and future trends,” International Journal of Production Economics, vol.145, no.1, pp.409-430, 2013 74 C. Sun, “Application of RFID technology for logistics on Internet of Things,” AASRI Procedia, vol.1, pp.106-111, 2012., 75 E. W. T. Ngai, K. K. Moon, F. J. Riggins, and C. Y. Yi, “RFID research: an academic literature review (1995–2005) and future research directions,” International Journal of Production Economics, vol.112, no.2, pp.510-520, 2008 76 Q. Zhu, R. Wang, Q. Chen, Y. Liu, and W. Qin, “IoT gateway: bridging wireless sensor networks into internet of things,” in Proceedings of IEEE/IFIP 8th International Conference on Embedded and Ubiquitous Computing (EUC), Dec 11-13, 2010, pp.347-352 77 R. van Kranenburg, E. Anzelmo, A. Bassi, D. Caprio, S. Dodson, and M. Ratto, “The Internet of things,” in Proceedings of 1st Berlin Symposium on Internet and Society, pp. 25-27, 2011 78 L. Atzori, A. Iera, and G. Morabito, “The Internet of things: a survey,” Computer Networks, vol.54, no.15, pp.2787-2805, 2010. 79 D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things: vision, applications and research challenges,” Ad Hoc Networks, vol.10, no.7, pp.1497-1516, 2012
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Power Wireless Personal Area Networks (6LoWPAN), Machine to Machine (M2M) and traditional IP
technologies such as IP, IPv6, etc.
C. Networks
Wireless Sensor and Actuator Networks (WSAN) or Ad Hoc Networks (AHNs)are some of the cross-layer
protocols that are used in Industrial IoT80. Things in IoT normally have diverse communication and
computation capabilities, as well as varying QoS requirements and this is why they should be revised before
being applied in IoT. However, the nodes in WSNs, normally have comparable requirements for hardware
and network communication. Additionally, in order to support information exchange and data
communication, the Internet is used by the IoT network. On the contrary, WSNs and AHNs are completely
internet-independent for their communications.
D. Service Management
Heterogonous objects are allowed in the IoT State-of-the-Art, as compatible services81. Instead, it is
required by the IoT dynamic nature, to provide reliable and consistent services. The impact caused by
device movement or battery failure can be minimized by an effective service-oriented architecture. The
OSGi platform82 is a good example on which a dynamic SoA architecture is applied in order to enable the
deployment of smart services. OSGi has been employed in diverse contexts (e.g., mobile apps, plug-in,
application servers, etc.), as an effective modular platform for service deployment. In this case, the Apache
Felix iPoJo implements the service composition based on an OSGi platform83.
2.9.1 Key IoT applications in industries
The use of IoT is rapidly evolving and growing. Quite a few IoT applications are being developed and
deployed in various industries including environmental monitoring, healthcare service, inventory and
production management, food supply chain, transportation, workplace and home support, security and
surveillance. Atzori et al.84 and Miorandi et al.85 provide a general introduction to IoT applications in various
domains. Comparing theirs, with our discussion, we specifically focus on industrial IoT applications. The
design of industrial IoT applications consider the following goals.
80 C. Han, J. M. Jornet, E. Fadel, and I. F. Akyildiz, “A cross-layer communication module for the Internet of Things,” Computer Networks, vol.57, no.3, pp. 622–633, 2013 81 D. Uckelmann, M. Harrison, and F. Michahelles, “An architectural approach towards the future internet of things”, in Architecting the Internet of Things, pp. 1-24, Springer, 2011 82 H. Cervantes, and R. S. Hall, “Automating service dependency management in a service-oriented component model,” in Proceedings of the 6th Workshop on Component-Based Software Engineering, May 2003, pp.1-5. 83 J. I. Vazquez, A. Almeida, I. Doamo, X. Laiseca, and P. Orduña, “Flexeo: an architecture for integrating Wireless Sensor Networks into the Internet of Things,” in Proceedings of 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008, pp. 219-228), 2009 84 L. Atzori, A. Iera, and G. Morabito, “The Internet of things: a survey,” Computer Networks, vol.54, no.15, pp.2787-2805, 2010 85 D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things: vision, applications and research challenges,” Ad Hoc Networks, vol.10, no.7, pp.1497-1516, 2012
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Layers Description
Sensing Integrated with existing hardware (RFID, sensors, actuators) to control/sense the
physical world and acquire data.
Networking Basic networking support and data transfer over wired or wireless network.
Service Creates and manages services. It provides services to satisfy user needs.
Interface Provides interaction methods to users and other applications.
Table 1: Design considerations for Industrial IoT Applications (adapted from Flügel & Gehrmann86)
Depending on the intended industrial application, designers may have to make a tradeoff among these goals
to achieve a balance of cost and benefits87. Below are some IoT applications in industries.
Using IoT in the healthcare service industry88. IoT provides new opportunities to improve healthcare89.
Powered by IoT’s ubiquitous identification, sensing, and communication capacities, all objects in the
healthcare systems (people, equipment, medicine, etc.) can be tracked and monitored constantly90. Enabled
by its global connectivity, all the healthcare related information (logistics, diagnosis, therapy, recovery,
medication, management, finance, and even daily activity) can be collected, managed, and shared efficiently.
For example, a patient’s heart rate can be collected by sensors from time to time and then sent to the
doctor’s office. By using the personal computing devices (laptop, mobile phone, tablet, etc.) and mobile
internet access (WiFi, 3G, LTE, etc.), the IoT-based healthcare services can be mobile and personalized91.
The wide spread of mobile internet service has expedited the development of the IoT-powered in-home
healthcare (IHH) services92. Security and privacy concerns are two major challenges that currently limit the
further development of IoT in health care.
Using IoT in food supply chain93. Today’s food supply chain (FSC) is extremely distributed and complex.
It has large geographical and temporal scale, complex operation processes, and large number of
stakeholders. The complexity has caused many issues in the quality management, operational efficiency,
and public food safety. IoT technologies offer promising potentials to address the traceability, visibility,
and controllability challenges. It can cover the FSC in the so-called farm-to-plate manner, from precise
agriculture, to food production, processing, storage, distribution, and consuming. Safer, more efficient, and
86 C. Flügel, and V. Gehrmann, “Scientific workshop 4: intelligent objects for the Internet of Things: Internet of Things-application of sensor networks in logistics,” Communications in Computer and Information Science, vol.32, pp.16-26, 2009 87 C. Flügel, and V. Gehrmann, “Scientific workshop 4: intelligent objects for the Internet of Things: Internet of Things-application of sensor networks in logistics,” Communications in Computer and Information Science, vol.32, pp.16-26, 2009 88 Z. Pang, Q. Chen, J. Tian, L. Zheng, and E. Dubrova, "Ecosystem analysis in the design of open platform-based in-home healthcare terminals towards the internet-of-things," in Proceedings of 2013 15th International Conference on Advanced Communication Technology (ICACT), Jan 27-30, 2013, pp.529-534 89 M. C. Domingo, “An overview of the Internet of Things for people with disabilities,” Journal of Network and Computer Applications, vol.35, no.2, pp.584-596, 2012 90 H. Alemdar, and C. Ersoy, “Wireless sensor networks for healthcare: a survey,” Computer Networks, vol.54, no.15, pp.2688-2710, 2010 91 I. Plaza, L. MartíN, S. Martin, and C. Medrano, “Mobile applications in an aging society: status and trends,” Journal of Systems and Software, vol.84, no.11, pp.1977-1988, 2011 92 Z. Pang, Q. Chen, J. Tian, L. Zheng, and E. Dubrova, "Ecosystem analysis in the design of open platform-based in-home healthcare terminals towards the internet-of-things," in Proceedings of 2013 15th International Conference on Advanced Communication Technology (ICACT), Jan 27-30, 2013, pp.529-534 93 Z. Pang, Q. Chen, W. Han, and L. Zheng, L., “Value-centric design of the internet-of-things solution for food supply chain: value creation, sensor portfolio and information fusion,” Information Systems Frontiers, in press, 2014
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sustainable FSCs are expectable in the future. A typical IoT solution for FSC (the so called Food-IoT)
comprises three parts: the field devices such as WSN nodes, RFID readers/tags, user interface terminals,
etc., the backbone system such as databases, servers, and many kinds of terminals connected by distributed
computer networks, etc., and the communication infrastructures such as WLAN, cellular satellite, power
line, Ethernet, etc. As the IoT system offers ubiquitous networking capacity, all of these elements can be
distributed throughout the entire FSC. Furthermore, it also offers effective sensing functionalities to track
and monitor the process of food production. The vast amount of raw data can be further mined and
analyzed to improve the business process and support decision making. Big data technologies can be used
to facilitate the challenge of analyzing the tremendous data collected from food supply chain.
Using IoT for safer mining production. Mine safety is a big concern for many countries due to the working
condition in the underground mines. To prevent and reduce accidents in the mining, there is a need to use
IoT technologies to sense mine disaster signals in order to make early warning, disaster forecasting, and
safety improvement of underground production possible94. By using RFID, WiFi, and other wireless
communications technology and devices to enable effective communication between surface and
underground, mining companies can track the location of underground miners and analyze critical safety
data collected from sensors to enhance safety measures. Another useful application is to use chemical and
biological sensors for the early disease detection and diagnosis of underground miners as they work in a
hazardous environment. These chemical and biological sensors can be used to acquire biological
information from human body and organs and to detect hazardous dust, harmful gases, and other
environmental hazards that will cause accidents. A challenge is that wireless devices need power and could
potentially detonate gas in the mine. More research is needed regarding safety characteristics of IoT devices
used in the mining production.
Using IoT in transportation and logistics. IoT will play an increasingly important role in transportation
and logistics industries95. As more and more physical objects are equipped with bar codes, RFID tags or
sensors, transportation and logistics companies can conduct real-time monitoring of the move of physical
objects from an origin to a destination across the entire supply chain including manufacturing, shipping,
distribution, and so on96. Furthermore, IoT is expected to offer promising solutions to transform
transportation systems and automobile services97. As vehicles have increasingly powerful sensing,
networking, communication, and data processing capabilities, IoT technologies can be used to enhance
these capabilities and share under-utilized resources among vehicles in the parking space or on the road.
For example, IoT technologies make it possible to track each vehicle’ existing location, monitor its
movement and predict its future location. Recently, an intelligent informatics system (iDrive system)
developed by BMW used various sensors and tags to monitor the environment such as tracking the vehicle
location and the road condition to provide driving directions98. Zhang et al.99 designed an intelligent
monitoring system to monitor temperature/humidity inside refrigerator trucks by using RFID tags, sensors,
and wireless communication technology. In the near future, we will see the development of an automotive
autopilot that can automatically detect pedestrians or other vehicles and take evasive steering to avoid
94 Q. Wei, S. Zhu, C. Du, “Study on key technologies of Internet of Things perceiving mine,” Procedia Engineering, vol.26, pp.2326-2333, 2011 95 L. Atzori, A. Iera, and G. Morabito, “The Internet of things: a survey,” Computer Networks, vol.54, no.15, pp.2787-2805, 2010 96 B. Karakostas, “A DNS architecture for the Internet of Things: a case study in transport logistics,” Procedia Computer Science, vol.19, pp.594-601, 2013 97 H. Zhou, B. Liu, and D. Wang, “Design and research of urban intelligent transportation system based on the Internet of Things,” Communications of Computer and Information Science, vol.312, pp.572-580, 2012 98 E. Qin, Y. Long, C. Zhang, and L. Huang, “Cloud computing and the Internet of Things: technology innovation in automobile service,” LNCS 8017, pp.173-180, 2013 99 Y. Zhang, B. Chen, and X. Lu, “Intelligent monitoring system on refrigerator trucks based on the Internet of Things,” Wireless Communications and Applications, vol.72, pp.201-206, 2012
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collisions as needed100. Security and privacy protection are important for the widespread use of IoT in
transportation and logistics since many vehicle drivers are worried about information leak and privacy
invasion. Reasonable efforts in technology, law and regulation are needed to prevent unauthorized access
to or disclosure of the privacy data.
Using IoT in firefighting. IoT has been used in the firefighting safety field to detect potential fire and
provide early warning for possible fire disasters. In China, RFID tags and/or bar codes are being attached
to firefighting products to develop nationwide firefighting product information databases and management
systems. By leveraging RFID tags, mobile RFID readers, intelligent video cameras, sensor networks, and
wireless communication networks, the firefighting authority or related organizations could perform
automatic diagnosis to realize real-time environmental monitoring, early fire warning and emergency rescue
as needed. Researchers in China are also using IoT technologies to construct Fire Automatic Alarming
Systems in order to raise the nation’s firefighting management and emergency management to a new level101.
Recently Ji & Qi102 illustrate an infrastructure of IoT applications used for emergency management in China.
Their IoT application infrastructure contains sense layer, transmission layer, support layer, platform layer,
and applications layer. Their IoT infrastructure has been designed to integrate both local-based and sector-
specific emergency systems. Establishing standards for implementing Fire IoT is a pressing challenge now.
2.9.2 IoT Solutions
Large public tech and telecom companies have been all over the IoT, which they rightly regard as something
that will truly move the needle for them over the next few years and possibly decades. It is entirely possible
that, in some cases, announcements are ahead of reality, but nonetheless the trend is clear. Chipmakers
(Intel, Qualcomm, ARM) are racing to dominate the IoT chip market. Cisco has been incredibly vocal about
the “Internet of Everything” and walked the talk with the Jasper. IBM announced an investment in a new
IoT business unit. AT&T has been aggressive in being the connectivity layer for cars, partnering with 8 out
10 top US car manufacturers. Many telecom companies view their upcoming 5G networks as the backbone
of the IoT. Apple, Microsoft and Samsung have been very active across the ecosystem, offering both
hubs/platforms (Homekit for Apple, SmartThings and an upcoming OS for Samsung, and Azure IoT for
Microsoft) and end products (Apple Watch for Apple, Gear VR and plenty of connected appliances for
Samsung and the upcoming HoloLens AR headset for Microsoft). Salesforce announced an IoT cloud a
few months ago. The list goes on and on.
Alphabet/Google and Amazon are probably worth mentioning separately because of the magnitude of
their potential impact. From Nest (home) to SideWalk Labs (smart cities) to autonomous cars to the
Google Cloud, Alphabet already covers huge portions of the ecosystem, and has invested billions in it. On
Amazon’s end, Amazon Web Services seem to be an ever increasing force that keeps innovating and
launching new products, including a new IoT platform this year which it inevitably will push aggressively
to become the backend for the IoT; in addition, the company’s eCommerce operations are increasingly
important to IoT products distribution, and Echo/Alexa is turning out to be a major sleeper hit for the
company in the home automation world. Both Alphabet and Amazon very much move at the speed of the
startups they were not so long ago, sit on immense amounts of user data, and have limitless access to top
100 C. G. Keller, T. Dang, H. Fritz, A. Joos, C. Rabe and D. M. Gavrila, “Active pedestrian safety by automatic braking and evasive steering,” IEEE Transactions on Intelligent Transportation System, vol. 12, no.4, pp. 1292 -1304, 2011. 101 Y.C. Zhang, and J. Yu, “A study on the fire IOT development strategy,” Procedia Engineering, vol.52, pp.314-319, 2013. 102 Z. Ji, and A. Qi, “The application of internet of things (IOT) in emergency management system in China,” in Proceedings of 2010 IEEE International Conference on Technologies for Homeland Security (HST), pp. 139-142, 2010
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talent. Some other worthy mentions are listed in brief: SAP, BOSCH, ThingWorx, Eclipse, Oracle,
Salesforce, IBM Bluemix, GE Predix.
2.10 Deep-learning
In the past decade, deep learning has been successfully applied in diverse areas including computer vision,
speech recognition, natural language processing, etc. The success of deep learning is attributed to its high
representational ability of input data, by using various layers of artificial neurals103. GPUs have played a key
role in the success of deep learning by significantly reducing the training time104. In order to increase the
efficiency in developing deep learning methods, there are a number of open-source deep learning toolkits
including Caffe from UC Berkeley105, CNTK from Microsoft106, TensorFlow from Google107, Torch108, and
many other tools like Theano109, MXNet110, etc. All these tools support multi-core CPUs and many-core
GPUs. One of the main tasks of deep learning is to learn a number of weights in each layer of network,
which can be implemented by vector or matrix operations. TensorFlow uses Eigen111 as accelerated matrix
operation library, while Caffe, CNTK, Torch employ OpenBLAS112 or cuBLAS113, to speed up matrix
related calculations. All the mentioned tools import cuDNN114, which is a GPU-accelerated deep learning
library, for their neural network computing. However, because of the difference of optimization methods
by vendors, these tools exhibit different running performance even when training the same neural network
on the same hardware platform. Furthermore, the performance of a tool also changes a lot when training
different types of networks, or using different types of hardware. Given the diversity of deep learning tools
and hardware platforms, it could be difficult for end users to choose an appropriate tool to carry out their
deep learning tasks. In this paper, we benchmark three major types of deep neural networks (i.e., fully
103 Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. 104 L. Deng, “Three classes of deep learning architectures and their applications: a tutorial survey,” APSIPA transactions on signal and information processing, 2012 105 Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 675–678 106 D. Yu, A. Eversole, M. Seltzer, K. Yao, Z. Huang, B. Guenter, O. Kuchaiev, Y. Zhang, F. Seide, H. Wang et al., “An introduction to computational networks and the computational network toolkit,” Technical report, Tech. Rep. MSR, Microsoft Research, 2014, 2014. research. microsoft. com/apps/pubs, Tech. Rep., 2014. 107 M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al., “Tensorflow: Largescale machine learning on heterogeneous systems, 2015,” Software available from tensorflow. org, vol. 1, 2015. 108 R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A matlablike environment for machine learning,” in BigLearn, NIPS Workshop, no. EPFL-CONF-192376, 2011 109 T. T. D. Team, R. Al-Rfou, G. Alain, A. Almahairi, C. Angermueller, D. Bahdanau, N. Ballas, F. Bastien, J. Bayer, A. Belikov et al., “Theano: A python framework for fast computation of mathematical expressions,” arXiv preprint arXiv:1605.02688, 2016. 110 T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang, “Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems,” arXiv preprint arXiv:1512.01274, 2015 111 Eigen,” http://eigen.tuxfamily.org/index.php, accessed: 2016-07-03 112 “Openblas,” http://www.openblas.net/, accessed: 2016-07-12. 113 C. Toolkit, “4.0 cublas library,” Nvidia Corporation, 2011.] to speed up matrix related calculations. All the mentioned tools import cuDNN. 114 S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer, “cudnn: Efficient primitives for deep learning,” arXiv preprint arXiv:1410.0759, 2014
networks (RNNs)119120121) on state-of-the-art GPU-accelerated tools (i.e., Caffe, CNTK, TensorFlow and
Torch), and analyze their advantage and disadvantage on both CPUs and GPUs, in terms of running time
performance. For each type of networks, we choose a small-size network and a large-size network for
evaluation.
Deep Learning refers to a class of machine learning (ML) techniques that combine the following:
Large neural networks (millions of free parameters)
High performance computing (thousands of processors running in parallel)
Big Data (e.g. millions of color images or recorded chess games)
Deep learning techniques currently achieve state of the art performance in a multitude of problem domains
(vision, audio, robotics, natural language processing, to name a few). Recent advances in Deep Learning
also incorporate ideas from statistical learning122123, reinforcement learning (RL)124, numerical optimization,
and broader fields125126.
In no particular order, here are some product categories made possible with today's deep learning
techniques: customized data compression, compressive sensing, data-driven sensor calibration,
offline AI, human-computer interaction, gaming, artistic assistants, unstructured data mining,
voice synthesis.
Customized data compression
If you are designing a video conferencing app and want to come up with a lossy encoding scheme to reduce
the number of packets you need to send over the Internet. You could use an off-the-shelf codec like H.264,
but H.264 is not optimal because it is calibrated for generic video—anything from cat videos to feature
films to clouds. More bytes can be saved with that rather than a generic algorithm if we take advantage of
the fact that most of the time, there is a face in the center of the screen. However, designing such an
encoding scheme is tricky. How do we specify where the face is positioned, how much eyebrow hair the
115 Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989 116 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105 117 D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1422–1432. 118 A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013, pp. 6645–6649 119 W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,” arXiv preprint arXiv:1409.2329, 2014 120 A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks.” in ICML, vol. 14, 2014, pp. 1764–1772. 121 D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1422–1432 122 Auto encoding variation all bayes [1312.6114] Auto-Encoding Variational Bayes 123 One shot deep generative 124 Deep Reinforcement Learning: Pong from Pixels 125 http://www.nature.com/nature/jou... 126 Deep Learning in Neural Networks: An Overview
4. Microsoft's AI can now understand speech better than humans
Speech recognition came a long way in 2016, with virtual assistants like Echo becoming hugely popular. In
October, research from Microsoft showed that, in comparing the ability of AI with humans in a
conversational speech task. Its system used convolutional and recurrent neural networks, trained on 2,000
hours of data, to achieve this victory.
5. AI predicts US election
While the outcome of the US presidential came as a huge surprise to many, even insiders, one AI system
saw it coming: An Indian startup in Mumbai called MogIA predicted a Trump win. The company analyzed
social media sentiment through 20 million social media data points. In this way, it may have tapped into
true voter preferences in a way that traditional polling did not. (held a "swarm AI" to predict the election,
which gave a narrow edge to Clinton. While inaccurate, given her lead in the popular vote—over 2.8 million,
as of this writing—it may not have been that far off, after all.). While some AI experts are cautious about
giving MogIA's victory too much weight, it was, nonetheless, able to predict an event that most humans
were not.
6. AI helps diagnose cancer
There have been major AI advances in healthcare. For instance: IBM Watson142 may be able to spot health
issues that your doctor can't. The cognitive machine once detected leukemia in a woman in Japan that had
been previously missed. Watson had proposed an additional diagnosis. And one AI program at the Houston
Methodist Research Institute in Texas reviewed millions of mammograms—30 times faster than humans—
and was able to achieve 99% accuracy in diagnosing cancer.
142 https://www.ibm.com/watson/
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3 Past and Ongoing Projects
In the following paragraph, the past projects, present projects, and initiatives are divided in three sections.
3.1 Past Projects
A list of tables, containing all relevant information on past projects that were related to the zero-defect manufacturing concept is presented in this paragraph. In each table, besides the details such as the duration or the budget, there is a section of all relevant publications.
MIDEMMA Minimizing defects in micro-manufacturing applications
Coordinator: IDEKO S. Coop (ES) No. of Partners: 17
Webpage: http://www.midemma.eu
Goal: The working methodology has been based on the development of extensive process
monitoring devices and techniques for the generation of a data rich environment.
Thanks to this data generation, adaptive process control methodologies and smart
decision making tools have been developed and applied in order to avoid defect
generation during micro-manufacturing processes.
Keywords: Zero-defect, monitoring
Summary/Hints: Extended final product validation to include raw material, workpiece and process monitoring and optimisation with technologies that enable defect detection in real time.
Demonstrations done at five end users from the medical devices and optics industries. Significant improvements were achieved in manufacturing of dental prostheses, optical components, ultrashort-pulse lasers, orthodontic components and micro-EDM machine tools.
4ZDM Cluster of 4 parallel projects (MIDEMMA, MUPROD, MEGAFIT and IFACOM) on dissemination actions.
Failed Objectives
/ Needs for
Improvements
Need for software developments close to industrialization.
Future integration/participation in EFFRA meetings
Relative
publications
Peer Reviewed Journals
2014
1. Bosmans N., Qian J., Reynaerts D., (2014). Reproducibility of a nanometre
accurate moving-scale measurement system, Key Engineering Materials 613, pp.
37-42.
2013
1. Claverley J. D., Leach R. K., (2013). Development of a three-dimensional
vibrating tactile probe for miniature CMMs. Precision Engineering 37, 2, pp. 491-
499.
2. Spieser A., Ivanov A., (2013). Recent developments and research challenges in
electrochemical micromachining (μECM). International Journal of Advanced
1. Guimarães R. A., Barceló T., Wagner T., Georgiadis A., (2014). Tool wear monitoring and prediction in micro milling process for medical applications. Experimental analysis and characterization of tool wear for titanium materials, in Proceedings of Sustainable Design and Manufacturing Conference (SDM-2014), pp. 589-600
2. Llanos I., Agirre A., Urreta H., (2014). Error Detection and Correction Methodology for Laser Milling Processes on Biocompatible Ceramic Materials, in Proceedings of Sustainable Design and Manufacturing Conference (SDM-2014), pp. 557-565
3. Wang J., Ferraris E., Galbiati M., Qian J., Raynaerts D., (2014). Simultaneously counting of positive and negative pulse parts to predict tool wear in micro-EDM milling, in Proceedings of the 9th International Conference on Micromanufacturing (ICOMM 2014).
2013
1. Bosmans N., Qian J., Reynaerts D. (2013). Random measurement errors of a nanometre accurate moving scale measurement system. In Schmitt, R. (Ed.), Bosse, H. (Ed.), 11th International Symposium on Measurement Technology and Intelligent Instruments (ISMTII): Vol. 1. ISMTII 2013. Aachen, 1-5 July 2013, pp. 243-244. Aachen: Apprimus Verlag
2. Bosmans N., Qian J., Reynaerts D. (2013). Long-term stability of a moving scale measurement system with nanometre uncertainty, ASPE2013
3. Bosmans N., Qian J., Reynaerts D. (2013). Determining the random measurement errors of a novel moving-scale measurement system with nanometre uncertainty. In Leach, R. (Ed.), Shore, P. (Ed.), Proceedings of the 13th international conference of the european society for precision engineering and nanotechnology: Vol. 1. Euspen 13th international conference. Berlin, 27-31 May 2013 (art.nr. P3.15), pp. 240-243. Bedford: euspen
4. Bosmans N., Tuytens J., Qian J., Reynaerts D. (2013). Abbe offset reduction for a nanometre accurate moving scale measurement system. In Blunt, L. (Ed.), Knapp, W. (Ed.), Laser metrology and machine performance X. Lambdamap 2013. Milton Keynes, 20-21 March 2013, pp. 91-100. Bedfordshire: Euspen
5. Claverley J. D., Leach R. K., (2013). Three-dimensional characterisation of a novel vibrating tactile probe for miniature CMMs, in Proceedings LAMBDAMAP 2013, pp. 257–266.
6. Eggebrecht M., Georgiadis A., Wagner T. (2013). Strategies for correcting the workpiece deformation during the manufacturing at the milling process, Leuphana University of Lueneburg, Institute of Product and Process Innovation (PPI), 2013 [AMA conference 2013]
7. Eggebrecht M., Georgiadis A., Wagner T. (2013). Strategies for correcting the workpiece deformation during the manufacturing at the milling process, In Schmitt, R. (Ed.), Bosse, H. (Ed.), 11th International Symposium on Measurement Technology and Intelligent Instruments (ISMTII): Vol. 1. ISMTII 2013. Aachen, 1-5 July 2013(pp. 243-244). Aachen: Apprimus Verlag
8. Hiersemenzel F., Claverley J. D., Petzing J. N., Leach R. K., Helmli K. S. (2013). Real surface topography measurement of high aspect ratio features using the focus variation technique, in HARMNST 2013.
9. Hiersemenzel F., Singh J., Petzing J. N., (2013). Development of a traceable performance verification route for optical micro- CMMs, in Proceedings LAMBDAMAP 2013, 2013, pp. 373–379.
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10. Hiersemenzel F., Claverley J. D., Petzing J., Helmli F. (2013). ISO compliant reference artefacts for the verification of focus varation-based optical micro-coordinate measuring machines. In Leach R. K. (Ed.), Shore P. (Ed.), Proceedings of the 13th International Conference of the European Society for Precision Engineering & Nanotechnology: Vol. 1. Euspen 13th international conference. Berlin, 27-31 May 2013 (art.nr. P2.16), pp148-151. Bedford: euspen.
11. Llanos I., Cilla J., Gómez M., Golzarri N., Urreta H. (2013). Tool wear control on micromilling operations for quality assurance during dental prostheses manufacturing, Euspen micro/nano manufacturing workshop. Karlsruhe, 27-28 November 2013.
12. Shizuka H., Riemer O., Rickens K., Brinksmeier E. (2013). Development of an On-Machine Tool Monitoring System by Using a High-Speed 2D Measurement Sensor. In Leach R. K. (Ed.), Shore P. (Ed.), Proceedings of the 13th International Conference of the European Society for Precision Engineering & Nanotechnology: Vol. 2. Euspen 13th international conference. Berlin, 27-31 May 2013 (art.nr. P5.18), pp121-124. Bedford: euspen.
13. Wang J., Ferraris E., Galbiati M., Qian J., Raynaerts D. (2013). Conbined Pulse Characterization and Discrimination for micro-EDM Milling Tool Wear Study. In Azcarate S. (Ed.), Dimov S. (Ed.), Proceedings of the 10th International Conference on Multi-Material Micro Manufacture (4M2013): San Sebastian, 8-10 October 2013, pp. 175-178. Singapore: Research Publishing
2012
1. Bosmans N., Qian J., Piot, J., Reynaerts D. (2012). Design of a precision measurement system using moving linear scales. In Shore, P. (Ed.), Spaan, H. (Ed.), Burke, T. (Ed.), Euspen 12th International Conference proceedings: Vol. 1. euspen 12th International Conference. Stockholm, 4-8 June 2012 (art.nr. O4.2), pp. 302-305. Bedford: Euspen
MEGaFiT Manufacturing Error-free Goods at First Time
o Single pixel OCT with galvo scanner for weld inspection (with commercial uptake);
o Thermography camera; o Spectrometer.
Numerical model of the process chains and pilot production lines.
Development and testing of a software platform to be coupled to the control system.
Main Results: o A methodology to reduce defects by better production control to improve
quality and reduce cost; o 2 pilot lines with HW and SW integration (including PLC with user
interface and embedded algorithms) demonstrating defect reduction from 5-15% to <1% from the health monitoring systems developed.
o 1 patent submitted on the “low coherence interferometry sensor system” for selective laser melting (SLM) AM technique.
Failed
Objectives /
Needs for
Improvements
Demonstration done in up to 3-step process, lacking demonstration in more
steps processes (despite the guidelines account for up to 20 steps
manufacturing).
Relative
publications
2014
1. Neef, A.; Seyda, V.; Herzog, D.; Emmelmann, C.; Schönleber, M.; Kogel-Hollacher, M.: Low Coherence Interferometry in Selective Laser Melting. In: Physics Procedia, 56(2014), pp. 82-89
2. Dallinger, F.; Roux, E.; Havinga, J.; d'Ippolito, R.; van Tijum, R.; van Ravenswaaij, R.; Hora, P.; van den Boogaard, T.: Adaptive process control strategy for a two-step bending process. In: Sustainable Design and Manufacturing 2014, (2014), Cardiff, Wales, UK, pp. 230-390
3. Dallinger, F.: Strain path dependent evolution of yield locus based on HAH model description. In: Sfar, H.; Maillard, A. (Edt.): Proc. International Deep Drawing Research Group Conf. IDDRG 2014, (2014), published
4. Stellin, T.; van Tijum, R.; Merklein, M.; Engel, U.: Study of a microforging process of parallel ribs from metal strip. In: Larkiola, J. (Edt.): Material Forming - ESAFORM 2014, (2014), pp. 565 – 572
5. Koops, R.; Sonin, P.; van Veghel, M.: Calibration standards to enable traceable in-process measurements of critical to quality product parameters. In: Proceedings Macroscale 2014, (2014), published
6. Reh, T.; Li, W.; Burke, J.; Bergmann, R.B.: Improving the Generic Camera Calibration technique by an extended model of calibration display. In: J. Europ. Opt. Soc., 9(2014)14044, published
2013
1. Renken, V.; von Freyberg, A.; Goch, G.: Potenziale in der Automatisierungstechnik durch Verbindung von speicherprogrammierbaren Steuerungen mit Methoden der künstlichen Intelligenz. In: Das Forum für Fachleute der Automatisierungstechnik aus Hochschulen und Wirtschaft: Tagungsband AALE 2013, 10. Fachkonferenz, , (2013), Stralsund: DIV, pp. 157-170
2. van Ravenswaaij, R.; van Tijum, R.; Hora, P.; van den Boogaard, T.; Engel, U.: Towards zero-defect manufacturing of smal metal parts. In: IDDRG
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2013 Conference Proceedings, (2013), Zurich: Institute of Virtual Manufacturing, ETH Zurich, pp. 87-92
3. Stellin, T.; Merklein, M.; Engel, U.: Investigation on a Microextrusion Process of Parallel Protrusions from Metal Strip. In: M. Jun, S. Park (Edt.): 8th International Conference on MicroManufacturing (ICOMM 2013), (2013), pp. 169-174
4. Havinga, G.T.; van den Boogaard, T.; Klaseboer, G.: Sequential Optimization of Strip Bending Process Using Multiquadric Radial Basis Function Surrogate Models. In: Key Engineering Materials, 554-557(2013), pp. 911-918
MuProD Innovative proactive Quality Control system for in-process multi-stage defect reduction
Coordinator: Fundacion Tecnalia Research & Innovation No. of Partners: 13
Webpage: http://www.muprod.eu/
Goal: This project aimed at developing an innovative Quality Control System that will drastically
change the current concept of End Of Line quality control, going beyond currently
established methodologies such as Six-sigma and SPC. The goal was to prevent the
generation of defects within the process at single stage and the propagation of defects
between processes at multi-stage system level. This Quality Control System is to be
proactive, offering three different solution strategies to avoid End of Line defects:
Elimination of the predicted defect through adjustment of process characteristics by proactively intervening on the inputs to the process (process parameters, etc.)
On-line reworking of the product in order to eliminate the defect
On-line workpiece repair through defect elimination at consecutive process stages.
Summary/Hints: 3 different solution strategies to avoid end of line (EOL) defects: o Elimination of predicted defect by proactively adjusting the process
parameters; o Online product reworking to eliminate the defect; o Online workpiece repair through defect elimination at consecutive process
stages.
3 real-life use cases: o Assembly chain of an electrical drive for sustainable mobility; o Precise large-part machining of components for the gearbox of windmill-
building industry; o Sustainable Micro-production of micro-intravascular catheters
Monitoring and prognosis process/machine models were developed.
New integrative solutions for proactive quality ontrol in correlated multi-stage systems were developed and demonstrated: o Correlation analyser; o Process-chain analyser; o Production/inspection planner; o System level defect manager.
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DynPro software utility was developed, integrating math models allowing immediate outputs on process parameters (non-complex input parameters).
ISP – information sharing platform for real-time collection, storage and analysis of data from sensor systems, machine controls or other software.
Defects reduction by 80% in electrical drive manufacturing demonstrator.
Relative
publications
Peer Reviewed Journals and Book Chapters
1. B.M. Colosimo, M. Pacella, N. SeninMultisensor Data Fusion via Gaussian Process Models for Dimensional and Geometric Verification. Submitted to Precision Engineering - November 2013
2. B.M. Colosimo, P. Cicorella, M. Pacella, M. Blaco, 2014, From profile to surface monitoring: SPC for cylindrical surfaces via Gaussian Processes, Journal of Quality Technology, Vol. 46, No. 2, April 2014, pp. 95-113
3. D. Coupek, A. Verl: Ausschussreduzierung und Defektkompensation in mehrstufigenProduktionssystemen. In: EffizienteProduktionFortschritt-Berichte VDI Reihe 2 Fertigungstechnik Nr. 689, ISBN 978-3-18-368902-6 [Titelanhanddieser ISBN in Citavi-Projektübernehmen]. Düsseldorf: VDI Verlag, 2014, 82-91
4. M. Colledani, D. Coupek, A. Lechler, J. Aichele, A. Yemane: Quality Oriented Assembly Strategy for Automotive Electric Drives. CIRP Journal of Manufacturing Science and Technology
5. M. Colledani, D. Coupek, A. Lechler, J. Aichele, A. Yemane: System Level Analysis of In-line Quality Improvement Strategies in the Production of Electric Drives. IEEE Transaction on Automation Science and Engineering.
Conference papers have also been submitted
IFaCOM Intelligent Fault Correction and self Optimizing Manufacturing systems
Sensor signal feature extraction in the time, frequency and time-frequency domains.
Several novel technological features were developed/demonstrated: o Insert cutters wear monitoring for torical milling tools; o Apparatus and method to minimize large parts deformations due to
clamping; o Sensorized arm for Robot Assisted Polishing; o Optical part measuring inside a milling machine; o …
A new working methodology was devised, based on: o Numerical control integrating and interfacing advanced sensors systems; o Measurement and quasi real-time compensation of machine tool geometric
errors; o Hardware system solutions for closed loop analysis and controlling of
manufacturing process parameters in real-time; o Efficient validation of many measurement processes/validation of
complex measurement processes; o Artificial Neural Network method for slurry control and optimization; o Online defect detection and process adjustment method and software for
WEDM; o …
Human-Machine interface and ActiveMQ Message broker for machine event monitoring-integration: consulting and customized development.
Increased predictability of quality and throughput/productivity due to increased data availability allowing for analysis and prediction of production’s outputs.
Reduced direct human labour costs.
Failed
Objectives /
Needs for
Improvements
A few drawbacks were noted (initial costs): o increased equipment cost and investments; o increased maintenance operations cost;
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Relative
publications
Online video
1. Malagola G., ‘Da SOMMACT a IFaCOM: verso lo Zero-defect
manufacturing’, INNOVARE magazine - Issue 3/2013, April 2013
2. Meier N., Georgiadis A., "Optical Part Measuring inside a Milling Machine",
Key Engineering Materials, Vol. 613, pp. 440-445, May. 2014
3. Arif M, Xirouchakis P, Ahmed B, Olcay A, Nenad N, Robert P, Stucki M.
A generic system architecture for simulation based intelligent multi-stage
multi-process machining system to achieve zero-defect manufacturing.
International Conference on Sustainable Design and Manufacturing (SDM),
Cardiff, United Kingdom, April 28-30, 2014
4. Morand M.P. , Master Thesis, “Experimental investigation of surface
integrity and defects for a quality assurance system of WEDMed aerospace
parts”, EPFL, 2014
5. Ferretti, S., Caputo, D., Penza, M., D'Addona, D.M., 2012, Strategies for
Zero Defect Manufacturing: an Overview, 8th CIRP Int. Conference on
Intelligent Computation in Manufacturing Engineering – CIRP ICME '12,
18-20 July, Ischia, Italy.
6. Di Foggia, M., D'Addona, D.M., 2012, Identification of Critical Key
Parameters and their Impact to Zero-defect Manufacturing in the
Investment Casting Process, 8th CIRP Int. Conference on Intelligent
Computation in Manufacturing Engineering – CIRP ICME '12, 18-20 July,
Goals: The main idea of ZeroWIN is that waste prevention has to be seen from a
holistic perspective to make it work efficiently and effectively. The goal is:
to develop innovative technologies, waste-prevention methodologies, strategies and system tools exportable into other European and worldwide contexts.
to develop a structured and innovative production model based on industrial symbiosis for resource-use optimisation and waste prevention, also taking residues as secondary raw materials
to demonstrate the innovative approach in practical demonstrators
Summary/Hints: 9 practical and 1 conceptual case study;
Regional collaboration among companies from traditionally separate sectors, which exchange by-products, energy, water and materials in such a way that waste from one industry becomes raw material for another;
waste-prevention methodologies, residues as secondary raw materials and innovative production model based on industrial symbiosis;
Goal: The main objective of the FP7 SOMMACT project was to contribute to the
transformation of the European manufacturing industries from a resource
intensive to knowledge intensive condition. For reaching this ambition, a new
machine tool concept was developed based on self-learning, adaptive control
capabilities and embedded traceable measurement capabilities.
SOMMACT develops and validates an innovative production hardware and
control system founded on understanding, evaluating and controlling large
machine tools production performances.
Summary/Hints: Detection (in-process embedded traceable measurements) and compensation (adaptive control and self-learning) of geometrical effects of varying external and internal quantities, such as temperature gradients and workpiece mass;
A new metrological concept to enhance the measuring capabilities of machine tools, redusing or avoiding QC-production loops;
Enhanced sensor systems for traceable on-machine inspection capability;
A self-learning model of the system performance;
Errors affecting machining accuracy reduced by more than 75 %;
Demonstrator where updating of compensation tables is performed in quasi-real time, subject to operator's confirmation;
Standardisation of compensation data (ISO 230-10:2011, ISO / WD TR 16907, ISO 3070 series);
Direct link with IFACOM project.
Assistance to decision making: o applying immediate action if unforeseen events; o requesting execution of retuning or full recalibration procedures if
machine state is outside the previously learned scenarios; o predicting periodical maintenance needs;
Relative
publications
Dissemination Report available online,
Final report available online
Technical articles:
Volumetric compensation of machine tool errors
Metrology provides reliability to industry
SOMMACT project starts experimental phase
Possible products biSLIDER concept Bisensor for Linearly Interpolating Differential error
recovery
PRO2CONTROL On-line Control of Drawing and Blanking Processes and of Quality of the
Product by Fusion of Sensors and Artificial Vision Techniques
Summary/Hints: Development of a complete control system fitted on the press-tooling assembly in order to assure a zero-defect in forming industries for small parts.
Detection of defects in real-time: o acoustic emission (AE); o load measurement technologies (sensor set mounted on the press and
the tooling); o artificial vision (AV) system.
online control of press state and tooling condition;
2 demonstrators;
Defective parts decreased from 0,1% to 0,08%;
Machine stop time reduced about 50%.
Publications Articles published in scientific journals
1. Saénz de Argandoña E., Aztiria A., García C., Arana N., Izaguirre A., Fillatreau P., Terzyk T. “Forming Processes Control by means of Artificial Intelligence Techniques” (accepted in Robotics and Computer-Integrated Manufacturing, accepted and to be published in summer 2008).
2. Saénz de Argandoña E., Aztiria A., García C., Arana N., Izaguirre A., Fillatreau P., Bernard F.X., Terzyk T. “Cooperation strategies between Artificial Vision System and Force-Acoustic sensors” (accepted in Robotics and Computer-Integrated Manufacturing, accepted and to be published in summer 2008).
3. Saenz de Argandoña E., Aztiria A., García C., Arana N., Izaguirre A. An Intelligent Controller using Expert Systems for Sheet Metal Forming Processes. Manufacturing Department, Computer Science Department, Mondragón University (submitted to Expert Systems with Applications).
Papers presented in international conferences
1. Sáenz de Argandoña, E., García C., Arana N., Izaguirre A., Aztiria A. “Control de proceso en tiempo real y aseguramiento de la calidad en operaciones de embutición y corte mediante adquisición de datos y técnicas de visión artificial”. XVI Congreso de máquinas-herramienta y tecnologías de fabricación, Donostia, España, October 2006.
2. Fillatreau P., Bernard Fx., Ardanza A., Arana N., Sáenz de Argandoña E., Izaguirre A., Garcia C., Mugarza J.C. "Calibrating Camera Position Parallel to a Surface for Dimension Calculation of Flat Parts", 8th International workshop on Electronics, Control, Modelling, Measurement and Signals 2007& Doctoral School (EDSYS, GEET), Liberec, Czech Republic, May 28-30, 2007.
3. Aztiria A., Saénz de Argandoña E., García C., Arana N., Izaguirre A. “Application of Artificial Intelligent Technique for Sheet Metal Forming processes global control”. 40th CIRP International Seminar on Manufacturing Systems, 2007, Liverpool, England, May 2007.
4. 4. Saénz de Argandoña E., Aztiria A., García C., Arana N., Izaguirre A., Fillatreau P., Terzyk T. “Control of forming processes by means of artificial intelligent techniques”. The 17th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2007, Philadelphia, USA, June 2007.
5. Saénz de Argandoña E., Aztiria A., García C., Arana N., Izaguirre A., Fillatreau P., Bernard F.X., Terzyk T. “Cooperation Strategies between
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Artificial Vision System and Force-Acoustic Sensors for Sheet Metal Forming Global Control”. The 17th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2007, Philadelphia, USA, June 2007.
6. Aztiria A., Saénz de Argandoña E., García C., Arana N., Izaguirre A. “Aplicación de técnicas de Inteligencia Artificial para el control global de procesos de conformado”. The 2nd Manufacturing Engineering Society International Conference, MESIC 2007, Madrid, España, July 2007.
7. García C., Sáenz de Argandoña E., Aztiria A., Arana N., Izaguirre A., Pop R., Galle M., Terzyk T., Fillatreau P. “Automatic detection of burrs in sheet metal cutting processes by a combination of a Sensor based Monitoring System, an Artificial Vision System and an Intelligent Control system”. 57th CIRP General Assembly, Dresden, Germany, August 2007.
8. Pop R., Saenz de Argandona E., Liewald M., Wagner S., Garcia C. ”Mit Prozesskontrolle zum Erfolg/ Künstliche Intelligenz zur Regelung von Stanzprozessen“ In: wt Werkstatttechnik online, 97th age-group (2007), 10th edition, SPRINGER-VDI-VERLAG.
9. Fillatreau P., Arana N., Sáenz de Argandoña E., Izaguirre A., Zuriarrain I., García C. “FPGA based Smart Camera For Industrial Production Control Applications”. Submitted to The 18th International Conference on Field Programmable Logic and Applications, Heidelberg, Germany, September 2008. (Waiting for acceptance.)
Technical reports
1. Control de procesos de conformado en tiempo real published in the Journal Adimendun (related with Intelligent Materials and Processes), nº12, July 2006, pages 1-4. 2.
2. Control de procesos industriales de conformado en tiempo real published in the Journal Información de máquinas-herramientas, equipos y accesorios IMHE, nº 329, October 2006, pages 10-15
SENS-IT Development of a low-cost permanently installed microelectronic wireless monitoring
Goal: Development of a Low-Cost Permanently Installed Microelectronic Wireless Monitoring System for Process Plant
Benefit: Improved monitoring techniques leading to lower operational costs, improved QHSE, more reliable information for increased integrity management, and zero loss of containment.
Summary/Hints: Non-intrusive, low-cost, global, and rapid Screening techniques based on Permanently Installed Monitoring (PIM)
Real-time, on-line monitoring and assessment of large areas of plant;
Failed
Objectives /
Low ability to sense defects, process information, and transmit this data via wireless methods to the plant operator;
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Needs for
Improvements
Relative
publications Power point presentation online
MEDEA Quality control for household appliances by on-line evaluation of mechanical defects
1. Stavrakakis G., Pouliezos A., Zervakis M., Anagnostakis E., Tselentis G., "Feature extraction and Neural-Fuzzy Classification of Washing Machines Vibration Signals for On-line Quality Control Purposes", EUFIT '97: European Congress on Intelligent Techniques, ELITE foundation, Aachen, Germany, pp. 1744-1748 (1997)
3.2 Ongoing Projects Similar outline applies for the tables that present the ongoing projects. Various information regarding the
project’s details, however no publications that have been published yet.
STREAM-0D Simulation in Real Time for Manufacturing with Zero Defects
Coordinator: Instituto Tecnologico de Aragon (ES) No. of Partners: 10
Webpage:
Keywords: Zero-defect, simulation, real-time
Goals: Facts: i) Zero-defect manufacturing and flexibility of production processes are
some of the main challenges for European manufacturing; ii) One of the
engineering tools with higher potential is the linking of simulation tools with
measurement devices for real-time control of applications. The huge potential of
this synergistic loop remains untapped for manufacturing processes and could be
used for reducing product variability, increase line flexibility and achieve zero
defect production.
These objectives could be reached by integrating in the production line multi-
physics simulation models, able to predict the product quality indicators in
response to the values of critical input parameters (components dimensions,
material properties, etc), which are unavoidably subject to variability: different
batches, different suppliers,... The models will be fed with actual data from online
measurements and, based on the model prediction, the critical steps of the line will
be controlled to adjust the product to the exact design specifications or to quickly
change specifications for producing customised batches. However, doing this in
real time is not possible due to the computational cost of models.
Reduced Order Modelling is a new generation of techniques which allow us to
obtain parametric solutions of complex models that can be particularized in real
time for any value of the parameters. The models run so fast that they can be
executed on tablets or smart phones. ROM will be used to transform complex
models into the real-time capable models that can be integrated in the production
line.
Moreover, the online deployment of ROMs and data gathering systems will
generate big data which will be exploited through data analysis techniques for
further improving the process.
The project will show proof-of-concept demonstrations in three real process
chains of the automotive sector, covering different types of production methods,
products, materials and manufacturing processes.
Summary/Hints: Production in-line integration of multi-physics simulation models.
Use of Reduced Order Modelling (ROM) techniques to convert complex models into real-time capable models that can be integrated in the production line (and run in tablets or smartphones).
GOODMAN aGent Oriented Zero Defect Multi-stage mANufacturing
Goals: Multi-stage manufacturing, which is typical in important industrial sectors such
as automotive, house hold appliance and semiconductor manufacturing just to
name few, is inherently complex. The main idea of GO0D MAN project is to
integrate and combine process and quality control for a multi –stage
manufacturing production into a distributed system architecture built on agent-
based Cyber-Physical Systems (CPS) and smart inspection tools designed to
support Zero-Defect Manufacturing (ZDM) strategies. Data analytics tools
provide a mean for knowledge build-up, system control and ZDM management.
Real time and early identification of deviations and trends, performed at local
level, allow to prevent the generation of defects at single stage and their
propagation to down-stream processes, enabling the global system to be
predictive (early detection of process faults) and proactive (self-adaptation to
different conditions).
The GO0D MAN project is based on the results of previous successful EU
projects and integrates them to realize and deploy a Zero Defect Manufacturing
framework for multi-stage production lines, in collaboration with industry
partners, a system integrator, two technology providers and three end users. The
use cases are representative of key European industrial sectors and have different
types of multi-stage production systems: the first use case concerns highly
automated serial mass production of automotive components, the second use
case is about batch production of high precision mechanical components for
automotive electro valves, the third use case produces professional customized
products such as ovens for restaurants. Successful completion of this project will
provide a replicable system architecture for ZDM. The results will be broadly
applicable in a variety of industries to improve the overall quality and
productivity of production systems.
Summary/Hints: Integrate and combine process and quality control into distributed system built on agent-based Cyber-Physical Systems (CPS) and smart inspection tools.
Predictive (early detection) and proactive (self-adaptation).
Use cases in automotive, high precision mechanical components, restaurant equipment.
Publications First publication on the project (in Italian)
ZAero Zero-defect manufacturing of composite parts in the aerospace industry
Coordinator: GKN Aerospace Norway (NO) No. of Partners: 13
Webpage: http://4zdm.eu/
Keywords: Zero-defect, multi-stage
Goals: Cluster (Focus Project) "Zero Defect Manufacturing" (ZDM) is a recent paradigm aiming at going beyond
traditional six-sigma approaches in highly technology intensive and strategic
European manufacturing sectors through new knowledge-based approaches.
The ZDM paradigm is of key importance to manage production quality targets in
advanced manufacturing industries. The implementation of this paradigm in industry
requires innovative defect management and control methods, novel technologies for
in-line inspection and integration of knowledge management and ICT tools for smart
and sustainable decisions in complex industrial scenarios, which are not available in
the market. The aim of the ForZDM project is to develop and demonstrate tools to
support the rapid deployment of ZDM solutions in industry and design more
competitive and robust multi-stage manufacturing systems.
Summary/Hints: Develop and demonstrate tools to support the rapid deployment of ZDM solutions in industry.
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Design more competitive and robust multi-stage manufacturing systems.
Combined adoption of new knowledge-based data-gathering and root-cause analysis solutions to reduce the generation of defects as well as new on-line defect management and improved production traceability solutions to mitigate the propagation of defects along the production line stages.
Proper integration of innovative enabling technologies, such as cyber-physical systems, selective inspection, advanced analytics and integrated process and part-flow control solutions.
Summary/Hints: developing a secure and reliable high-tech automatic wrapping machine based on an innovative IoT platform;
Innovative IoT platform, integrated in a re-designed automatic wrapping machine, ensuring higher productivity, onsite and remote monitoring of the whole machine and of the single components;
Reliable, scalable, flexible and customizable wrapping process, with 25% reduction on environmental impact;
Project starts at TRL6.
AMAZE Additive Manufacturing Aiming Towards Zero Waste & Efficient Production of High-
Goal: The overarching goal of AMAZE is to rapidly produce large defect-free additively-manufactured (AM) metallic components up to 2 metres in size, ideally with close to zero waste, for use in the following high-tech sectors namely: aeronautics, space, automotive, nuclear fusion and tooling.
Summary/Hints: Bring additive manufacturing by layer-upon-layer melt deposition of advanced alloys to the mainstream of industry fabrication;
R&D, design work, quality control and standardisation (ASTM, ISO and ECSS standards and certification protocols);
Cooperation with ISO TC261/ ASTM F42 Joint Group 59 on NDT for AM parts;
significantly suppress the number of interfaces and assembly steps;
50% cost reduction for finished AM parts;
<5% level of defects is the project objective;
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Integration of new techniques such as adaptronics, actuator systems, robotic automation, in-situ sensing, novel post-processing;
AMAZE Design/Process/Materials Database;
Commercial software package capable of modelling and predicting AM processing, component properties, performance and life-time, as well as tolerance to defects
APOD industrial demonstrator;
Four pilot-scale, streamlined, integrated AM factories;
Tenfold increase in build speed can be achieved;
Process monitoring being undertaken (in-process 3D scanning and melt pool monitoring);
Laser-ultrasonic testing (LUT) NDT methods to detect artificially generated defects in laser powder bed fusion AM samples;
Gemba Kaizan methods to promote major waste reduction;
Linkage to Eureopean technology clusters activities;
2 patents filed;
Failed
Objectives /
Needs for
Improvements
Some processes are still suffering from an unacceptable level of defects;
Some batches of material have been quarantined due to non-compliance and remedial is underway;
Publications Manfredi, D., Calignano, F., Krishnan, M, Canali, R, Ambrosio, E.P., Sara
Biamino2, Ugues, D, Pavese, M. and Fino, P.,Additive Manufacturing of Al Alloys and
Goals: The continuous trend towards miniaturization and multi-functionality embedded in products and processes calls for an ever increasing innovation, research and development within the European manufacturing sector. A necessary condition for the European productive sector to be at the global forefront of technology, ensuring job creation and sustainable growth, is to have access to innovative, entrepreneurial, highly skilled research engineers in the fields of micro manufacturing and micro product/process development. The MICROMAN ITN will provide world excellent research training to 13 ESR in the field of micro manufacturing.
Summary/Hints: Innovative, entrepreneurial, highly skilled research engineers in the fields of micro manufacturing and micro product/process development
excellent research training to 13 ESR o innovative technological solutions for high quality and high throughput
micro production (micro manufacturing process fingerprint, zero-defect net-shape micro manufacturing) for the micro manufacturing industry;
o cutting edge inter-disciplinary training in different domains (µ-polymer moulding, µ-metal forming, µ-extrusion, µ-tooling technologies, µ-product metrology, µ-manufacturing process metrology);
o validation of different micro manufacturing processes by integration into process chains for the production of micro component for the bio-medical, health-care, machine tool, pharmaceutical, quality control sectors;
50-30-20 principle (core, complementary & general competences);
3.3 Initiatives
Additionally to past and present projects, there is a very important initiative that aims to gather all zero-
defect manufacturing project partners, in order to create a cluster that will disseminate and spread the
necessity of zero-defect manufacturing, as well as the results these projects bring to the actual industry.
Summary/Hints: Build upon the fundament of five existing FoF Clusters, Zero Defect Manufacturing (4ZDM), Robotics, Clean factory, Precision Micro Production Technologies (High Micro) and Maintenance and support
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Working on the following four objectives: o Provide pro-active support to the projects in the participating clusters to
disseminate the project’s tangible outcomes to raise the awareness and thus increasing on a short term the industrial exploitation and take-up (“As-is”).;
o Elaborate on the common ground of the clusters to establish a European state-of-the-art and world-wide technology watch to inform the European manufacturing industry constantly while also formulating (with support from cluster specific top-ranked experts) the future FoF priorities based upon jointly identified business trends and market prospects (“As-is”);
o Deliver a proven model and associated methodology for effective cluster creation, execution and monitoring based upon the experience of the five participating clusters in FOCUS. This methodology will considerably ease the process of creating cluster thus maximizing the possibilities of increasing the impact of ‘exploiting’ cross-project synergies (“To-be”);
o Deliver a model and associated methodology to ensure industrial exploitation and industrial take-up for future projects including guidelines for all stakeholders including the European Commission, project initiators and partners (“To-be”).
Final outcome FOCUS model for clustering and industrial exploitation known at relevant stakeholders
Methodology for clustering
Methodology for industrial exploitation & take-up
Lesson learnt from the existing clusters
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4 Trends in Manufacturing Intelligence
The resurgence in the industrial sector post economic slowdown coupled with the growing trend towards
automated manufacturing units is instrumental in driving demand for metrology solutions and services.
Furthermore, advancements in measurement technologies and mounting preference for portable metrology
solutions have further fueled market momentum144. Technavio’s143 market research analysts have predicted
that the global 3D metrology systems market will grow steadily during the forecast period and will post a
CAGR of more than 7% by 2020. Credence research estimates that the global metrology services market is
expected to expand at a CAGR of 7.9% from 2016 to 2023, exceeding US$ 985.0 Mn by 2023144. The trends
in the business models are Value for Money, Innovating to Zero, and Smart is the New Green145.
Figure 12: Global Metrology Services Market Revenue and Compound Annual Growth Rate, 2014-2023. Source: Credence
Reseach
The dimensional metrology market segmentation by product can be done as follows: CMM (coordinate
measuring machine), optical digitizers, 3D scanners and Laser tracker among others146.
Manufacturing Intelligence compromises a R&D program established to develop the next generation of
manufacturing and processing technologies, led by industry147. Increasing market pressure towards quality,
efficiency and flexibility together with new developments in ICT, Artificial Intelligence (AI) and
optimization techniques have led to the concept of intelligent manufacturing. Intelligent manufacturing is
also known as smart manufacturing, being used almost interchangeably.
143 Global 3D Metrology Systems Market 2016-2020, Technavio, 2016. [Online] Available: http://www.technavio.com/report/global-embedded-systems-global-3d-metrology-systems-market-2016-2020 144 Metrology Services Market by end-use application, Credence Research, 2016. [Online] Available: http://www.credenceresearch.com/report/metrology-services-market 145 Analysis of the Global Dimensional Metrology Market in the Automotive Industry, Frost Sullivan, 2014. 146 Metrology Market, by Type, by Application, Reign Market Forecast to 2027, 2016. [Online] Available: http://www.kltv.com/story/33742419/metrology-market-by-type-by-application-manufacturing-automotive-aerospace-consumer-products-heavy-industryby-reign-market-forecast-to-2027 147 Nagy, D, Jering, D., Strasser, T., Martel, A., Garello, P., and Filios, E., “Intelligent Manufacturing Systems - Impact Report.” 2005.
A comprehensive definition of smart/intelligent manufacturing is presented as follows: “Smart
manufacturing (or intelligent manufacturing) is a data intensive application of information technology at
the shop floor level and above to enable intelligent, efficient and responsive operations”148.
Today, the focus is increasingly shifting towards a combination of reconfigurability, flexibility and even
adaptability149,150. During manufacturing, monitoring, diagnostics and measures like predictive maintenance
play an important role151. Overall, continuous improvement is crucial to make the system intelligent.
Especially the rapid developments in AI, ML and Big Data in combination with advanced sensor technology
are promising areas to further the knowledge about and at the same time the intelligence of manufacturing
systems.
Data collection and integration through sensors used to measure physical parameters of an
environment or specific objects. Examples for parameters include temperature, pressure, humidity, velocity
and acceleration. The quality of collected data is limited by the properties of the sensors. The sensors have
become ubiquitous. During 2005, Gartner have predicted that “By 2015, wirelessly networked sensors in
everything we own will form a new Web. But it will only be of value if the ‘terabyte torrent’ of data it
generates can be collected, analysed and interpreted”152. The integration of sensors into the existing data
space of the Web, is the major prerequisite for utilizing sensor generated streams of data as an innovative
and key source of knowledge.
A set of research efforts have been made towards the semantic uplift of stream data, i.e., by the W3C
Semantic Sensor Network Incubator Group153 and154,155,156. The major objective is the semantic uplift of
stream data and their availability according to the Linked Data principles157 – a concept widely known as
Linked Stream Data158, which facilitates the seamless and effective integration, not only between
heterogeneous sensor datasets, but also between sensor and Linked Data collections, providing valuable
information to a new range of “real-time” applications.
The cell phone industry has been the single largest driver of new CMOS image sensor technology for the
past ten years—smaller pixels, higher sensitivity, and lower noise—all in a bid to decrease sensor cost and
capture ever higher quality still and video imagery for human consumption. The size of the cell phone
market has enabled the tremendous investment in the fabrication technologies required to achieve these
advances. Smaller, adjacent markets like machine vision and medical imaging have taken advantage of this
148 Wallace, E. and Riddick, F, “Panel on Enabling Smart Manufacturing (presentation),” presented at the APMS 2013, Washington D.C, 11-Sep-2013. 149 H. A. ElMaraghy, “Flexible and reconfigurable manufacturing systems paradigms,” Int. J. Flex. Manuf. Syst., vol. 17, no. 4, pp. 261–276, 2005. 150 M. Peschl, N. Link, M. Hoffmeister, G. Gonçalves, and F. L. Almeida, “Designing and implementation of an intelligent manufacturing system,” J. Ind. Eng. Manag., vol. 4, no. 4, pp. 718–745, 2011. 151 Mazumder, J., “INTELLIGENT MANUFACTURING: ROLE OF LASERS AND OPTICS,” 2008, p. 6. 152 M. Raskino, J. Fenn, and A. Linden, “Extracting value from the massively connected world of 2015,” Gart. Inc, vol. 1, 2005. 153 “W3C Semantic Sensor Network Incubator Group.” 2008. 154 E. Bouillet, M. Feblowitz, Z. Liu, A. Ranganathan, A. Riabov, and F. Ye, “A semantics-based middleware for
utilizing heterogeneous sensor networks,” in Distributed Computing in Sensor Systems, Springer, 2007, pp. 174–
188.
155 A. Sheth, C. Henson, and S. S. Sahoo, “Semantic sensor web,” Internet Comput. IEEE, vol. 12, no. 4, pp. 78–
83, 2008.
156 K. Whitehouse, F. Zhao, and J. Liu, “Semantic streams: A framework for composable semantic interpretation
of sensor data,” in Wireless Sensor Networks, Springer, 2006, pp. 5–20.
157 C. Bizer, T. Heath, and T. Berners-Lee, “Linked data-the story so far,” Int. J. Semantic Web Inf. Syst., vol. 5, no. 3, pp. 1–22, 2009. 158 J. F. Sequeda and O. Corcho, “Linked stream data: A position paper,” 2009.
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massive technology investment by re-using the same technologies. The recent proliferation of Global
Shutter image sensors targeted to machine vision applications is a prime example of such technology
leverageError! Bookmark not defined..
Simulation and forecasting can support improvement-oriented decision-making throughout the life
cycle. In this context, we use ‘simulations’ specifically for predictions based on models representing the
systems and the mechanisms governing their behaviour159, and the more general ‘forecasts’ for predictions
based on the course of variables without considering underlying mechanisms (thus potentially providing
more superficial insights) – e.g. extrapolation160. Computer simulation is an established means of virtual
testing during product and process development (e.g., 161). Based on the results, designers can identify
problems and accept or reject design choices. Simulation approaches exist for many artefact behaviours,
covering practically any phenomenon of physics separately or combined as Multiphysics.
Disadvantages of interactive simulations are that human subjects and hardware are expensive162,163 and that
the whole simulation has to run in real time to run in sync with the subjects, which means that neither (i)
faster-than-real-time simulation to evaluate lots of scenarios or design alternatives nor (ii) slow simulations
with computation-intensive models (e.g. finite-element Multiphysics) are possibleError! Bookmark not defined..
In additive manufacturing, to realize high quality parts advanced simulation and modeling tools are crucial
for accelerating the quality assurance/quality control process to know if there is any defect as soon as
possible to avoid extra cost, time and waste. A combination of physics-based tools and data driven models
are needed to rapidly qualify a part164.
159 R. E. Shannon, “Introduction to the art and science of simulation,” presented at the Proceedings of the 30th conference on Winter simulation, 1998, pp. 7–14. 160 J. S. Armstrong, “Forecasting by extrapolation: Conclusions from 25 years of research,” Interfaces, vol. 14, no. 6, pp. 52–66, 1984. 161 H. Van der Auweraer, “Frontloading design engineering through virtual prototyping and virtual reality: industrial applications,” presented at the TMCE, 2008, vol. 1, pp. 39–52. 162 R. W. Pew and A. S. Mavor, Human-system integration in the system development process: a new look. Washington, DC: The National Academic Press, 2007. 163 G. C. Burdea, “Haptics issues in virtual environments,” presented at the Computer Graphics International, 2000, pp. 295–302. 164 Innovation in Manufacturing, Bharat Parihar, June 2016. [Online] Available: https://innovation-in-manufacturing.deloitte.com/2016/06/13/the-challenges-of-quality-control-in-additive-manufacturing/
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5 New potential opportunities and possible threats (SWOT analysis)
To analyze the new potential opportunities and possible threats that Z-fact0r will face in its
commercialization face it is important to take into consideration the addressed market and the unique selling
of the product. The project is being developed to be adapted accordingly to the market needs and therefore
it will be the ultimate system for the integral management of the manufacturing industry seeking zero-
defect.
A SWOT analysis was performed in the proposal phase, in order to analyze the current situation and the
market landscape. In this analysis, the helpful and harmful factors for the Z-fact0r have been identified.
At this point of the project, another analysis has been done but this time focused on new potential
opportunities and possible threats in terms of commercialization. The possible threats/competition have
been identified in terms of the technologies that are recently being commercialized after research projects
as well as already commercialized products in previous paragraphs. The opportunities correspond to the
introduction of the relevant Z-fact0r developments which can be considered as a potential opportunity at
the particular market.
The main innovative aspect of Z-fact0r is that it is an integral solution for the manufacturing industry.
This is the advantage which makes the difference between other systems already in the market and Z-fact0r.
The Z-Fact0r solution comprises the introduction of five (5) multi-stage production-based strategies
targeting (i) the early detection of the defect (Z-DETECT), (ii) the prediction of the defect generation (Z-
PREDICT), (iii) the prevention of defect generation by recalibrating the production line (multi-stage), as
well as defect propagation in later stages of the production (Z-PREVENT), (iv) the
reworking/remanufacturing of the product, if this is possible, using additive and subtractive manufacturing
techniques (Z-REPAIR) and (v) the management of the aforementioned strategies through event
modelling, KPIs (key performance indicators) monitoring and real-time decision support (Z-MANAGE).
The integration of these 5 strategies in the same approach is the unique selling point of Z-fact0r with no
competitors in the market presenting similar systems.
At the moment, a number of EU and international standards are under development considering the target
of zero-defect manufacturing. Gradually, all production-related equipment will have to comply with these
standards in order to be certified for use within the European Union. As a result, there is a significant
opportunity to work towards this direction. In Z-Fact0r, we will ensure that all systems will be compliant
with the identified relevant, under-development standards, from organizations like DIN/DKE, ETSI,
ISO/IEC and OASIS.
The strategies and methodologies developed in Z-Fact0r will enormously facilitate the implementation of
Z-Fact0r in real production lines. Although the project will focus on three specific applications, the
methodology and guidelines generated in the project will allow its introduction in many other industrial
settings, by demonstrating the system in three industries with similar characteristics.
From an industrial point of view, the main obstacle hindering the scalability of the proposed approach is
its holistic inspection and monitoring to achieve zero-defects, which requires individual studies respect to
the target solution. In this sense, given a new application to be covered, the Z-Fact0r models will have to
be fine-tuned ad-hoc for that application, taking into account the environmental conditions involved, the
process variables, and the dimensional or material parameters of the product being manufactured. The
target specifications will be specific of the product (a dimension, geometry, a force value, an input-output
force curve, etc.). Besides, the control systems are designed based on the target specs, the outcome of the
simulation and the specific stages of the chain that need to be controlled.
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Nevertheless, Z-Fact0r by its nature is designed to be scalable and adaptable to different cases, in order to
tackle the different customer needs. The demonstration of Z-Fact0r to three different use-cases from
different industries will provide sufficient knowledge to allow faster self-adaptation, proving the system
capacity to be universal.
Over the last decade, many companies have developed simulation models of products or processes that are
being used in the design and development phases, even at pre-production level. These models can be the
basis of the application of the Z-Fact0r solution in their production lines. The following obstacle will be
the transition to Z-Fact0r’s new prediction and simulation models needed for each particular application.
However, the documentation and the long evaluation of the requirements for setting-up Z-Fact0r will allow
a roadmap of simple steps to install technologies developed in the project allowing its quick development
for any application. Moreover, Z-Fact0r sets a premium to any manufacturing process, by correlating in
real-time the product level with the machine one, allowing the optimization of the whole production
according to the specific goals, such as increased customer satisfaction, reduced costs, reduced energy
consumption, etc.
Nowadays, most production lines have already many online sensors and data acquisition systems. The
progress of Industry 4.0 over the next years will exponentially increase the number of cyber-physical
systems in the production lines, thus facilitating the implementation of Z-Fact0r after the project. A similar
assertion can be made about cloud based systems.
Based on the above and as a result of the market search we have performed, it has been verified that existing
solutions focus on empowering operations personnel and manager to take decisions. No vendor/ service
provider seems to offer solutions that can either support autonomous decision-making at nearly real-time,
or advanced inference on the basis of diverse data streams and product/workstation models.
The following table collects all the identified aspects which constitute the SWOT Analysis focused on the
new technologies developed and commercialization of Z-fact0r.
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POSITIVE NEGATIVE
INT
ER
NA
L
Strengths
Innovative integral approach (5 multi-stage production-based): Z-DETECT, Z-PREDICT, Z-PREVENT, Z-REPAIR and Z-MANAGE
Autonomous hierarchical decision support
Autonomous diagnosis capabilities, including root cause analysis, (realized by the ES-DSS) aligned with both the production context (infrastructure, equipment) and the product (quality specifications and actual status).
Trend analysis techniques and Ensemble learning for long term-optimization and process monitoring with the Early Malfunction Analysis.
Innovative solution for portable scanning inspection. Diverse data analysis optical scanning, and visualization, HMI, prediction, etc.
Build point-clouds of ROI (Region of Interest) at high speed.
Integration of CM techniques and CEP analysis.
Intelligent deburring techniques with the development of robotic intelligence
Designed to be scalable and adaptable to different cases, in order to tackle the different customer needs.
Computational power
Ability to handle distributed and heterogeneous data sources
Weaknesses
Transition to Z-Fact0r’s new prediction and simulation models needed for each particular application.
On-line inspection Tools for understanding, monitoring, analysis and real-time fault diagnosis of industrial process operation and product quality.
Disjointed due to nature of multi-stage
Vulnerable to external influence
Computational burden
Increased data storage needs
Algorithms with increased complexity
Initially there is a need to monitor Multiple factors
EX
TE
RN
AL
Opportunities
No vendor/ service provider offers solutions that can either support autonomous decision-making at nearly real-time, or advanced inference on the basis of diverse data streams and product/workstation models.
Pioneer in EU and international standards in development.
Manufacturing analytics reap benefits from the massive potential of the Internet of Things (IoT) at shop-floor, enterprise and supply chain level.
Know-how developed during the project in Additive Manufacturing techniques
The progress of Industry 4.0 over the next years will exponentially increase the number of cyber-physical systems in the production lines, thus facilitating the implementation of Z-Fact0r after the project. A similar assertion can be made about cloud based systems.
Sensorial network with novel self-adjustment
Manufacturing sector looks for reduced waste and energy consumption, reduced cost and customer satisfaction
Threats
Most production lines have already many online sensors and data acquisition systems.
Increased options to purchase products from outside Europe (Asia, the US, etc.)
Competition in dimensional metrology (Metrologic group, Mahr Federal, Carl Zeiss, Kreon, Hexagon Metrology...), PLC (Mitsubishi, Rockwell, Schneider, Siemens…) and other companies developing similar technologies.
Competition in solutions developed in other research projects (MIDEMMA, MEGaFiT, GOODMAN, ZAero…) as outlined in paragraph 3
Table 2: Identified aspects of the SWOT analysis
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As the project progresses, the SWOT analysis will be enhanced and more directed to the product (taking
into account its features) that is to be commercialized with respect to the competition.
If, for example, a particular technology -that is somewhat relevant to what z-fact0r is developing-, is now a
feature in a competitor product, then it can be included as a (commercial) possible threat/competition. If
it is not, then the introduction of the relevant z-fact0r developments can be considered as a (commercial)
potential opportunity at the respective market. An example of this approach is described schematically
below.
Table 3: Approach on future developments
Companies A B C
Products product A product B product C
List of Results of z-fact0r/Features in product to be commercialised
i -
includes
relevant
technology
- -
ii - - - -
n - - - -
List of Results of z-fact0r/Features in product to be commercialised
Strength Points Weak Points Opportunities Threats
i
a relevant
technology
exists in
product A
iino similar
feature
in competition
nno similar
feature
in competition
SWOT ANALYSIS
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6 Conclusions
In the present deliverable, we have presented an extensive analysis on the State of the Art of the
technologies that are being used in the Z-fact0r project, the past and existing projects, as well as the
initiatives that are in the sector of the zero defect manufacturing, and finally a detailed analysis of Trends
in Manufacturing Intelligence as well as, a SWOT analysis.
In the State of the Art analysis, we presented in detail the following technologies:
Decision Support System
Early Malfunction detection and analysis
Real-time optimization
Green scheduling
Laser scanning and 3D vision
SCADA
Programmable Logic Controllers
Discrete event simulation
Industrial IoT
Deep Learning
These technologies are all going to be used in the Z-fact0r project, with an aim to surpass the current
State of the Art and reach new goals.
In the next paragraph, there is a detailed list of 10 past projects, 8 ongoing projects and one initiative
directly associated with the zero-defect manufacturing projects. In the detailed tables, there is information
such as budget, duration as well as goals and publications.
Finally, there is a paragraph where the current Trends in Manufacturing Intelligence are presented in
extensively, and a detailed SWOT analysis that focuses on the new potential opportunities and possible
