Digital Twins Technology IDA Mechanical & IPU · Digital Twins for Condition-Based Maintenance of...

Digital Twins Technology – IDA Mechanical & IPU

2019-03-26 Digital Twins Technology - IDA Mechanical & IPU 1

17:00 – 17:05 Welcome & Introductions

17:05 – 17:20 Introducing IPU & Digital Twin Technology

– Søren Merit, CEO

17:20 – 17:45 Digital Twins for Condition Based Maintenance of

Refrigeration Containers

– Ragnar Ingi Jónsson, Specialist Engineer

17:45 – 18:15 Break – Sandwich & Networking

18:15 – 18:35 Model-in-Loop Software Development for

Automation of Heavy Duty Machinery

– Kevin Rice, Senior R&D Engineer

18:35 – 18:55 Virtual Models for Product Analysis and

Manufacturing Processes

– Nikolas Aulin Paldan, Specialist Engineer

18:55 – 19:00 Final Remarks & Questions

Introducing IPU & Digital Twin Technology

Søren Merit

CEO at IPU,

Technology Driven

Business Innovation,

M.Sc., B.Com.

(+45) 40 90 46 30

[email protected]

We are a spin-off of the Technical University of Denmark

70% Technology development for

industry

30% Research projects

50% of projects are we working

with DTU researchers

130 million DKK in donations

supporting DTU research

Started in

1956 by 4 DTU professors

Independent commercial

foundation

Purpose to facilitate use of

new technology in Danish

industry

2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, [email protected]

We Develop Solutions to Complex Technology Challenges

We help our clients speed up development

… and reduce technical risks

… and manufacturing uncertainties

Discovery Basic Research Applied Research

Product and

Manufacturing

Development

Production

• Technology Search

• Proof of Concept

• Feasibility Study

• Test setup

• Data analysis

• Prototyping & development

• Modelling & Simulations

• Digital Twin

• Specialists in multi-disciplinary product- and manufacturing technology development

• Team of international specialists


IPU Key Expertises

2019-03-26 Introducing IPU & Digital Twin Technology, Søren Merit, [email protected] 5

We can help you…

developing and optimizing materials

and surfaces processes for specific

purposes, including analyzing

problems and malfunctions.

Our core strengths are metallic and

polymer materials as well as

electroplating and corrosion

Materials Types and Choices

Surface Treatment

Corrosion and Wear

Protection

Software and simulation

Modelling of Cooling

Processes

Energy Optimisation

We can help you…

analyzing thermodynamic and heat

transfer processes and their

components.

We optimize system performance

and efficiency using tailored

simulation models and the latest

R&D expertise.

Digital twins

Fault detection

FEM analysis

Machine learning

We can help you…

developing digital models of

physical system in order to perform

simulations in a fast and safe

environment.

We develop digital twins, perform

big data analyses, decision

algorithms (AI), machine learning

and visual pattern recognition

Advanced materials

and surfaces

Thermodynamics

and energy

Physical systems

modelling

Software & Algorithm

Development

Condition-Based Monitoring

System Analysis

We can help you…

modelling, analyzing and

developing complex autonomous

systems, robotics and automation

of systems and processes.

Combining development of

mechanical design and hardware

with control systems and software

Autonomous

systems and

automation

How We Work with Complex Systems


Understand

physics

Multi physics

modelling Prototyping

and tests

Data

analysis Machine

Learning

Understand business case and process

We Develop Solutions to Complex Technology Challenges


IPU has developed a

software solution

offering predictive

maintenance and

conditions-based fault

detection of refrigerated

containers enabling

significant savings in

costs related to physical

inspection.

IPU has developed an

automation concept for

the new ESO telescope,

using a safe chemical

cleaning process, that

strips the mirror coating

during planned

maintenance, without

altering the fragile mirror

substrate.

IPU has developed an autonomous

systems solution for heavy duty

construction machinery. IPU developed

automation control system, retrofit

hardware components, develop control

software and operator user interface.

Digital twin based hardware and software

in the loop development

Digital Twin Technology

Welcome to the Most Hyped Technology!

Gartner’s Hype Cycle, August 2018


What is a Digital Twin?

Digital model of the elements and

dynamics of how a product, process or

service operates

10

..applied in development

..and in operations


What is Digital Twin?

Digital model of the elements and

dynamics of how a product, process or

service operates

11

Development Operations

• CAD interacting with multi-

physical simulations

• Developing and testing

software (Model-in-Loop)

• Developing and testing

components

(Hardware-in-loop)

• Installation: Calibrating and

adjusting

• Monitoring: Comparing

sensor data with simulation

results

• Optimizing: Adjusting

system for wear and

external conditions


Why use Digital Twins

12

Automotive

Aerospace

Benefits of digital twins

• Faster insights – Fail fast succeed faster

• Cheaper and faster tests

• Feasible to explore extreme conditions

• Understand dynamics better

• Ability to predict and adjust – during operations

• Faster update with minimal stops in operations


Digital Twins for Condition-Based

Maintenance of Refrigeration Containers

Ragnar Ingi Jónsson

Specialist Engineer at IPU,

Physical System Modelling &

Conditions-Based Monitoring,

M.Sc., Ph.D.

(+45) 45 25 41 86

[email protected]

Maersk Motivation & Goals

2019-03-26 Digital Twins for Condition-Based Maintenance of Refrigeration Containers, Ragnar Ingi Jónsson, [email protected] 14

• Remote Container Management (RCM)

• Connectivity and transparency – being in control

• Improved customer experience – documentation

• Monetary savings – maintenance and operation

How can we improve the efficiency of the

reefer maintenance operations, cargo

safety and energy consumption?

• Vast funds spent on pre-trip inspections (PTI)

approx. US$ 750 yearly, per unit.

Timeline & Overview


• 2010 IPU/Maersk collaboration on Reefer Alarm

Prediction System (RAPS/ePTI) of the RCM system

• 2012 Agreement with Ericsson and AT&T for

hardware and data infrastructure.

• Satellite communication installed on 400 vessels

• Local GSM communication between container and

vessels

• 2015 RCM system launch may 1st.

• 2018 IPU to update RAPS/ePTI with new reefer

models, detections updates and other features.

RAPS / ePTI – Step-by-Step


1. Sensor readings are collected and saved on

each reefer.

2. Reefer data is send to vessel via on-board

local GSM or commercial GSM while in land

3. Satellite communication sends reefer data

to head quarters

4. Individual reefer data is processed through

simulation models and fault detection

algorithms.

5. Alarms and warnings are reviewed and

appropriate actions taken service

ordered if needed.

Reefer Models for Fault Detection


• Model requirements

• Fast calculation models

• Include all major properties

• Basic refrigeration system

• Heat uptake, heat release, power

consumption

• High and low pressure parts

• Internal temperatures

• Other temperatures included

(ambient, cooling water, reefer, set point)

• The general refrigeration system model

• Refrigeration type determines, compressor type, whether there is e.g. internal heat exchange,

economizer etc.

Fault Detection Algorithm Overview


Normal operation Fault


Model

Residual

(difference)

Statistical

Detection

Threshold

Comparison

Detection

Threshold

Normal

Behavior

Faulty

Behavior

Detection Results

measured data

time stamp

operational data

other…

measured data

not used in

model

simulated

values component-wise

residual data

setpoint

ambient conditions

cargo info

etc.

Reefer Data

Statistical Change Detection Overview

• Statistical model of normal and faulty behavior, and

signal-to-noise ratio affects the detection time

• Cumulative log-likelihood is typically used with a

cumulative threshold



15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:000

20

40

60

80

100

120

140

160

180

200

time [hour:minute]

Evap. decis.

Pdisc decis.


Statistical Change Detection Example

• Reduced flow of air in evaporator


Normal Fault

Normal Fault

Normal Fault

IPU Deliveries & Results for Maersk

• Backend computational core of the RAPS/ePTU systems, processing approximately 200.000

hourly updates providing alarms and warnings to monitoring systems.

• Calibrated high performance models of all reefer refrigeration system and their variants

• The majority of technical issues detected before cargo is affected ($), efficiency of the reefer

maintenance operations could be improved by over 40%


ePTI which was developed in close coordination with IPU, has not only resulted in significant direct cost

reductions. ePTI also gives Maersk Line full cold chain transparency, improves and optimizes operational

processes and offers faster turn times benefiting our customers. All this whilst ensuring that the equipment we

release for our customer is in fully cargo worthy condition suitable for transport of temperature sensitive cargo.

We look forward to further develop the ePTI algorithm together with IPU to get the full benefits from the huge

amount of data now made available through RCM.

— Lars-Henrik Jensen, Operations Manager, Remote Container Management, Maersk Line

“

“

Lessons Learned & Recommendations

• Technical Recomentations

• Exploit your physical understanding of the problem or data-set – especially when developing the digital

twin of refrigeration system.

• Balance the amount of time used on cleaning up data and improving the detection algorithm.

• Time is on your side, a longer timeline will limit false predictions.

• Ensure constant alignment with the process – physical experiments are needed.

• Organization and project alignment

• Secure the easy wins first and build from there based on risk assessment, FMEA, experience.

• Business case with Digital twin – ensure the efforts brings value to the organization.

• Align with stakeholders and involve their knowledge, inputs and ideas if applicable.

• It will most likely never be perfect, set reasonable goals.


Break – Sandwich & Networking

Model-in-Loop Software Development for

Automation of Heavy Duty Machinery

Kevin Rice

Senior R&D Egineer at IPU,

Autonomous System,

Mechatronics and Controls,

M.Sc.

(+45) 29 93 47 93

[email protected]

Automation Project Overview (On-going & Confidential)

• Costumer Owns and Operates Heavy-Duty Machinery

• Task to develop automation solution to ensure high machine efficiency.

2019-03-26 Model-in-Loop Software Development for Automation of Heavy Duty Machinery, Kevin Rice, [email protected] 25

Operator & GUI

Automation

Controller Construction

Machine

Sensor &

Positioning

Actuator

Input

System Data

& User Input

Difficult to find skilled operators. Training takes time

with high chance of new operators leaving.

• Automation Controls

System Architecture

• Retrofit Machine with

new Hardware

• Automation software

and User Interface

Software development with Digital Twin


• Large efforts on automation software

• Adaptive/learning path planning depending on soil hardness.

• Sensor algorithms – fusion techniques, global/local location,

redundant sensors for safety.

• Safe-zone of operation – not damaging itself or surrounding

objects.

• Software complexity, and low availability of machine,

demands development with Digital Twin

• Hardware-in-Loop (HiL)

development.

• Model-in-Loop (MiL)

development.

Developing the Digital Twin


• Digital Twin must represent the actual system through Cyber-Physical Modelling

• Excavator dynamics / kinematics, hydraulic actuation system, sensor noise, …

• Certain Components modelled in hardware – electronics of sensor, valve dynamics, …

[1] [2]

Digital Twin and Physical System Mismatch

• Digital Twin will deviate from physical system

• Sufficient accuracy is obtained from system

understanding, engineering intuition and

experience.

• Digital Twin accuracy vs. Modelling Efforts vs.

Simulation Time should be considered

• Digital model mismatch results in higher

software quality

• Algorithms are implemented to support model and

physical system behavior.

• Typically through calibration options, resulting in

software supporting variations

(Manufacturing, mechanical wear, etc.)


[3]

Model-in-Loop Development Workflow

• System and Integration testing is fast with MiL

development. Limiting time consuming physical

tests.

• Continuous Integration testing with simulated

operating conditions, and failure modes.

• High confidence in algorithms in other software

functionality.


Requirements

& Specifications

System

Design

Architecture

Design

Module

Design

Implementation

Functional &

Unit Testing

Integration

Testing

System

Testing

Acceptance

Testing

Implementation Unit and

functional testing

System & integration

testing

Model-in-Loop Recommendation & Final Remarks

• Fast development flow, and possible to development physical system in parallel with software.

• Testing all operating modes, including failure modes, is fast with Digital twins and Simulation.

(Expensive and time consuming tests on physical system, safety issues, …)

• Integration and system test should be performed on physical system in parallel with

development on digital twin. Software and control strategies will require online tuning


Mechanical & Electronics

Software Design

Time – Traditional Development

Mechanical & Electronics

Software Design Smarter Products

Time – Development with MiL

[1] H. Feng, C. B. Yin, W. Weng, W. Ma, J. Zhou, W. Jia & Z. Zhang, Mechanical Systems and Signal Processing, Volume 105, 15 May 2018, Pages 153-168

[2] T. O Andersen, Department of Energy Technology Lecture Notes, Aalborg University, 2nd Edition, 2003.

[2] L. Schmidt, PhD Dissertation, Department of Energy Technology, Aalborg University, 2014

Virtual Models for Product Analysis

and Manufacturing Processes

Nikolas Aulin Paldan

Specialist Engineer at IPU,

Integrated Product &

Process Technology,

M.Sc.

(+45) 45 25 46 16

[email protected]

Topics

• Background & Introduction

• Virtual models for product analysis &

manufacturing processes

• Example of Virtual Process Model

• Summary

2019-03-26 Virtual Models for Product Analysis and Manufacturing Processes, Nikolas Aulin Paldan, [email protected] 32

Background & Introduction I

• At IPU for 15 years, Specialist engineer in Integrated Product & Process Technology

• Background is in mechanical engineering for equipment and tooling.


Background & Introduction II

• Heavy user of numerical tools to create virtual models of processes performed by machines

and tooling.

• Many types of virtual models. Examples shown here will be focused on models for Finite

Element Analysis.


Calculation of steady state tool temperature in a cyclic polymer welding process

35

Virtual Models for Product Analysis

& Manufacturing Processes

Optimizing Manufacturing Processes Using Virtual Models


Design of Process

Equipment

Process

Optimization

& Test

Process

Modelling

Possibility to Speed-Up Development and Reducing Testing Efforts


Design Verification Testing

Less than 100% workload

Virtual Design

Verification

testing

38

Example of Virtual Process Model

• For IPU Use numerical models in many projects.

• Due to confidentiality agreements IPU examples can not be shown…

• Example from a totally different business area, in which they have fully embraced the use of

virtual models:

• Reduced testing of spot-welds in Automotive manufacture

• The optimization of the spot-welding process normally requires a lot of physical testing –

being able to do testing on virtual models has been widely adapted.


Spot-welding in Cars (Tesla Production Line)


Youtube.com

• The following slides borrowed from the Company Swantec

• Spin-out from DTU in 1999, software SORPAS for the simulation of resistance welding –

costumers


Material Challenges for Automotive Spotwelding Processes

• Conventional steels - Relative simple to

spotweld

• Mild steels

• Interstitial free (IF) steels

• Bake hardenable (BH) steels

• High strength low alloy (HSLA) steels

• Carbon Manganese (CMn) steels


• Advanced High strength steels (AHSS) -

Can be very difficult to spotweld

• DP – Dual Phase

• CP – Complex Phase

• TRIP – (TRansformation Induced Plasticity)

• Mart. – Martensite steel

• TWIP – (Twinning Induced Plasticity)

• 3rd Gen AHSS

Principle of Resistance Welding Regarding Current


dttItRtQ

t

0

2 )()()(Joule heating:

Principle of Resistance Welding Regarding Force


(3)

Weld

F I

(2)

Squeeze

F F

(4)

Hold

(5)

Weldability Lobe – Welding Process Window


Welding current

Fo

rce

Minimum

weld size Acceptable

weld size

Expulsion

Zone

Typical

Nugget failures

Importance of Welding Process Optimization


Undersized

weld

Acceptable

weld size

Expulsion

Zone

Spot Welding - Three Sheets of Steel


Welding Process Optimization – Welding Ranges

• Spot welding mild steel sheets


Weld Growth Curve Weldability Lobe

Weld Strength Testing and Failure Modes


• Weld strengths

• Tensile shear strength

• Cross tension strength

• Peel strength

• Failure modes

• Plug (button) failure

• Interface failure

Plug failure Interface failure



• Tensile-shear test – plug failure



• Weld strengths based on welding process simulation

• Output weld strengths for structural and crash modeling

Final Remarks & Questions

Nikolas Aulin Paldan

Specialist Engineer

(+45) 45 25 46 16

[email protected]

Kevin Rice

Senior R&D Engineer

(+45) 29 93 47 93

[email protected]

Ragnar Ingi Jónsson

Specialist Engineer

(+45) 45 25 41 86

[email protected]

Søren Merit

CEO

(+45) 40 90 46 30

[email protected]

… see more cases at ipu.dk

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