562 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND … · Lihui Wang Abstract—This paper presents an...

562 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 4, JULY 2008

Wise-ShopFloor: An Integrated Approachfor Web-Based Collaborative Manufacturing

Lihui Wang

Abstract—This paper presents an integrated approach forWeb-based collaborative manufacturing, including distributedprocess planning, dynamic scheduling, real-time monitoring, andremote control. It is enabled by a Web-based integrated sensor-driven e-ShopFloor (Wise-ShopFloor) framework targeting dis-tributed yet collaborative manufacturing environments. Utilizingthe latest Java technologies (Java 3D and Java Servlet) for systemimplementation, this approach allows users to plan and control dis-tant shop floor operations based on runtime information from theshop floor. The objective of this research is to develop methodologyand algorithms for Web-based collaborative planning and control,supported by real-time monitoring for dynamic scheduling. De-tails on the principle of the Wise-ShopFloor framework, systemarchitecture, and a proof-of-concept prototype are reported in thispaper. An example of distributed process planning for remote ma-chining is chosen as a case study to demonstrate the effectivenessof this approach toward Web-based collaborative manufacturing.

Index Terms—Function block (FB), process planning, remotemachining, scheduling, Web-based real-time monitoring.

I. INTRODUCTION

R ECENTLY, collaborative manufacturing has emerged asthe norm of manufacturing in a distributed environment.

This is largely due to the global business decentralization andmanufacturing outsourcing. To stay competitive in the dynamicglobal market, companies with distributed factories or divisionsare demanding a new way of effective collaborations amongthemselves and even between their suppliers and outsourcedservice providers. Among many other factors, flexibility, time-liness, and adaptability are identified in this research as themajor characteristics to bring dynamism to collaborative man-ufacturing. Distributed manufacturing processes are complex,especially at machining shop floors where a large variety ofproducts, usually in small batch sizes, are handled dynamically.The dynamic environment requires an adaptive system architec-ture that enables distributed planning, dynamic scheduling, real-time monitoring, and remote control. It should be responsive toboth varying collaboration needs and unpredictable changes ofdistributed production capacity and functionality. An ideal shopfloor should be the one that uses real-time manufacturing intel-ligence to achieve the best overall performance with the leastunscheduled downtime. However, traditional methods are based

Manuscript received October 17, 2006; revised April 26, 2007. This paperwas recommended by Associate Editor R. Brennan.

The author is with the Integrated Manufacturing Technologies Institute,National Research Council of Canada, London, ON N6G 4X8, Canada(e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSMCC.2008.923868

on offline processing that is normally performed in advance, andthus, are impractical if applied directly to this dynamic collab-orative environment. In response to the requirements and to co-ordinate the dynamic activities in collaborative manufacturing,a sensor-driven and Web-based planning and control approachis needed to achieve the dynamism in the distributed manufac-turing environment.

The objective of this research is to develop methodolo-gies and a Web-based integrated sensor-driven e-ShopFloor(Wise-ShopFloor) framework for distributed planning, dynamicscheduling, real-time monitoring, and remote control supportedby sensors, function blocks (FBs), Java technologies, and theWeb infrastructure. The Wise-ShopFloor is designed to usethe popular client–server architecture, as well as view-control-model (VCM) and publish–subscribe design patterns for ef-fective information sharing during collaborative planning andcontrol.

This paper is organized as follows. In Section II, enablingtechnologies including Web, Internet, Java 3D, and Java servletsare introduced based on a brief literature review. It is followed bya description of the Wise-ShopFloor framework in Section III.Details on adaptive and distributed process planning are pre-sented in Section IV, which leads to Web-based real-time mon-itoring and control documented in Section V. A case studyusing planning results for Web-based remote machining is de-scribed in Section VI. Finally, contributions are summarized inSection VII.

II. REVIEW OF ENABLING TECHNOLOGIES

With the growing manufacturing decentralization, productsand services might be distributed everywhere and sourced any-where along supply chains. Product design and fabrication haveshifted rapidly from intracorporation to global networks. How tocoordinate manufacturing activities and keep them under controlis a challenging issue. Flexibility, timeliness, and adaptabilityof manufacturing operations are the essential requirements forcollaborative manufacturing in such a dynamic environment.Fortunately, the Web infrastructure today is mature enough toform a distributed manufacturing network through client–serverinterconnections. During the past decade, the Web has beenwidely used for development of collaborative applications tosupport dispersed working groups and organizations because ofits platform, network, and operating system transparency, and itseasy-to-use user interface—the Web browser. In addition to theWeb technology, Java has brought about a fundamental changein the way that applications are designed and deployed. Java’s“write once, run anywhere” model has reduced the complex-ity and cost traditionally associated with producing software

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WANG: WISE-SHOPFLOOR: AN INTEGRATED APPROACH FOR WEB-BASED COLLABORATIVE MANUFACTURING 563

solutions on multiple distinct hardware platforms. With Java,the browser paradigm has emerged as a compelling way to pro-duce collaborative applications over the Web. Examples includeWebCADET [1] for collaborative design and CyberCut [2] forrapid machining among many other efforts [3]–[7]. In terms oftechnologies used in the existing systems, Hypertext MarkupLanguage (HTML), Java applets, ActiveX, and Virtual RealityModeling Language (VRML) are widely adopted for develop-ing client-side user interfaces. At the server side, technologiesincluding JavaServer Pages (JSP), Java servlets, and Extensi-ble Markup Language (XML) are quickly obtaining attentionsfor new system development. To facilitate a viable collabora-tive system, its application server must engage users in a 3-Dgraphical interaction in addition to the dialog-like data sharing,because remote users need active and visual aids to coordinatetheir efforts in a distributed environment.

Today, collaborative manufacturing tops the wish list formany manufacturers. Unfortunately, most of the manufactur-ing equipment of today does not have the built-in capabilityto transmit and receive data. Few of the available Web-basedsystems are designed for shop floor monitoring and control orfor advanced factory automation. Some related systems listednext are limited in their functionality and platform requirements.The latest Cimplicity from GE Fanuc Automation U.S. allowsusers to view their factory’s operational processes through anXML-based WebView screen, including all alerts on every Cim-plicity system [8]. The FactoryFlow from Unigraphics Solu-tions (USA) is an offline factory-floor layout planning, materialhandling, and simulation package [9]. By most estimates, thenumber of computer numerical control (CNC) machines capa-ble of linking to the Internet is less than 10% of the installedbase [9]. Seeking the opportunity in linking CNC machineswith the Internet, MDSI (Ann Arbor, MI) uses OpenCNC [10],a Windows-based software-only machine tool controller withreal-time database, to automatically collect and publish machineand process data on a network. In 1999, Hitachi Seiki (Japan)introduced FlexLink [11] to its turning and machining centers.Working together with PC-DNC Plus from Refresh Your Mem-ory U.S., FlexLink is able to do in-process gauging, machinemonitoring, and cycle-time analysis. Since 1998, Mazak (Japan)has been operating its high-tech Cyber Factory concept [12] at itsheadquarters in Oguchi, Japan. The fully networkable MazatrolFusion controllers allow Mazak machines to communicate overwireless factory networks for applications including real-timemachine tool monitoring and diagnostics. In addition, Japan-based Mori Seiki introduced a CAPS-NET system that pollsmachine tools on Ethernet at settable increments, usually 5 sor longer, for engineers to get updates on machine tools’ run-time status in production [13]. To bring legacy machine toolswith only serial ports online, e-Manufacturing Networks, Inc.(now Memex Division, Hamilton, ON, Canada), introduced itsION Universal Interface and CORTEX Gateway [14] to help theold systems go online, and to monitor information flow and thestatus of the CNC machine tools on the network.

As summarized in Table I, despite all the accomplishments,the available systems are either for offline simulation or formonitoring only. Most systems require a specific application to

TABLE IAVAILABLE SYSTEMS/PRODUCTS AND THEIR FUNCTIONALITIES

be installed instead of a standard Web browser, which reduces asystem’s portability. Remote shop floor monitoring and controlremain impractical as Web-based applications due to securityconcerns and the real-time constraints. Reducing network traf-fic, increasing system performance, and overcoming securitybarriers are the major concerns in Web-based manufacturingsystem developments.

To bridge the gap, a sensor-driven approach is proposed in thisresearch for real-time monitoring so as to facilitate Web-basedplanning and control for collaborative manufacturing. Web andJava technologies are also adopted in this research as the en-abling technologies for collaborative manufacturing realization.In the current implementation, a thin-client user interface hasbeen developed as a Java applet that runs inside a Web browser.Java 3D has been used to model a physical device that can re-place or supplement cameras in providing visual help duringremote planning, monitoring, and control. A set of decision-making logics have been designed as server-side componentsfor multiclient collaborations. For example, a Java 3D modelcan communicate with server-side Java servlets for real-timemonitoring. Details on how the different technologies can worktogether are explained in Section III.

III. WISE-SHOPFLOOR FRAMEWORK

The Wise-ShopFloor framework has been designed to provideusers with a Web-based and sensor-driven intuitive environmentwhere distributed process planning, dynamic scheduling, real-time monitoring, and remote control are undertaken. Withinthe framework, each machine should become an informationnode and be a valuable resource in the information network. Adirect connection to sensors and machine controllers is used tocontinuously monitor, track, compare, and analyze productionparameters. Instead of camera images (usually large in datasize), a physical device of interest (e.g., a milling machine) canbe represented by a Java 3D scene graph model with behavioralcontrol nodes embedded. Once downloaded from its applicationserver, the 3-D model is rendered by the local CPU and can workon behalf of its remote counterpart showing real behavior forvisualization at a client side. It remains alive by connecting withthe physical device (via servlets) through low-volume message



Fig. 1. Scope of Wise-ShopFloor.

passing (sensor data). In addition to motion data, other sensorydata including temperature, vibration, and force can also betransmitted via network and shown in colors or contour lineson the 3-D model for machine condition monitoring. As the3-D model is entirely driven by the sensor data and renderedlocally for visualization, there is no need of transmitting cameraimages over the Internet. The largely reduced network trafficmakes real-time monitoring and remote control practical fordispersed users connected to a shared Cyber Workspace [15]. Italso enables engineers to make accurate decisions in a timelymanner, and to ensure that machines are operating within thedefined expectations. Being able to plan and control dynamicshop floor operations from anywhere at anytime collaborativelyis what this research is aiming at. Fig. 1 illustrates the scope ofthe Wise-ShopFloor.

As a constituent component in manufacturing supply chain,the Wise-ShopFloor links physical shop floors with the uppermanufacturing systems. Similar to the e-manufacturing and e-business, the four major Wise-ShopFloor activities shown inFig. 1 are conducted via a collaborative cyber workspace.

The interactions among the modules are illustrated in moredetail in Fig. 2, where the framework has been designed into aclient–server architecture using VCM design pattern with built-in secure session control. The mid-tier application server han-dles major security concerns, such as session control, sessionregistration, sensor data collection/distribution, planning andscheduling, as well as real device manipulation. A central Ses-sion Manager has been designed to look after the issues of userauthentication, session synchronization, and sensitive data log-ging. All initial transactions need to go through the SessionManager for access authorization. In a multiclient environment,different users may require different sets of data or logic fordifferent tasks. For example, in the case of monitoring, it is notefficient to have multiple users who share the same model talk-ing with the same device at the same time. Publish–subscribedesign pattern is adopted to collect and distribute sensor dataat the right time to the right user, efficiently. As a server-sidemodule, the Signal Collector is responsible for sensor data col-lection from networked physical devices. The collected data arethen passed to another server-side module Signal Publisher who

Fig. 2. Wise-ShopFloor framework.

in turn multicasts the sensor data to the registered subscribers(clients) through applet–servlet communication. A Registrar hasbeen designed to maintain a list of subscribers with the re-quested sensor data. A Java 3D model can thus communicateindirectly with sensors no matter where the client is, inside afirewall or outside. Hypertext Transfer Protocol (HTTP) stream-ing is chosen as the communication protocol between server andclients.

Although the global behaviors of a Java 3D model arecontrolled by the server based on real-time sensor signals,users still have the flexibility of viewing the model from dif-ferent perspectives (zooming, orbiting, panning, tilting, etc.)at a client side. In order to control a device, an autho-rized user can send control commands to the applicationserver that in turn manipulates the physical device. Althoughthe Wise-ShopFloor framework provides an alternative ofcamera-based monitoring through Java 3D models, an off-the-shelf Web-ready camera can easily be switched on remotelyto capture unpredictable (unmodeled) scenes for diagnosticpurposes.

Note that Java is used for communication and data sharingbetween clients and the application server. At this layer, thereal-time constraints are less critical if compared with thoseat device level. Although Java can be used for data collectionand machine control at the device level, users still have theflexibility of using a stripped-down version of C or any otherlanguage that is closer to the device level for practical imple-mentations. As used in the case study in Section VI, the sensordata collection and servo control are implemented in C++. TheWise-ShopFloor makes heterogeneous use of languages andmodules possible, as long as the same defined data format isused

IV. DISTRIBUTED PROCESS PLANNING AND SCHEDULING

The four business modules shown in Fig. 1 are interrelated,i.e., the output of one module may be the input of another. Fordynamic scheduling, real-time information from the monitoringmodule plays an important role. For the sake of page limitation,



Fig. 3. Architecture for distributed process planning.

only the distributed process planning is presented, leaving aninterface to the scheduling open.

A. Architecture Design

Fig. 3 shows the detailed architecture of the process planningmodule. Within the Wise-ShopFloor, the approach for processplanning is realized by a two-layer structure of shop-level su-pervisory planning and machine-level operation planning. Aprocess plan generally consists of two parts: generic data (ma-chining method, machining sequence, and machining strategy)and machine-specific data (tool data, cutting parameters, andtool paths). Such a two-layer structure is, therefore, consideredsuitable to separate the generic data from those machine-specificones. Since the resources, knowledge/database, and decision-making are logically and geographically distributed, such aprocess planning approach is also named distributed processplanning (DPP) [16].

The supervisory planning focuses on product data anal-ysis, machining feature (m-feature) parsing, setup planning,machining process sequencing, and machine selection, whilethe operation planning considers jig/fixture selection and thedetailed working steps for each machining operations, includ-ing cutting tool selection, cutting parameters assignment, toolpath planning, and control code generation. At the supervisoryplanning stage, the decisions made are generic and applica-ble to all machines. Process optimization is only performed atthe operation planning stage when specific resources (machine,tool, and fixture) are known and within a relatively small searchspace. Task distributions to the best available machines are dealtwith by the dynamic scheduling module.

B. Machining Process Sequencing

One critical task in process planning is machining sequencegeneration. Since a part design can be decomposed into basic m-features (such as hole, slot, pocket, etc.) either through feature-based design or via a third-party feature recognition solution, thetask of machining process sequencing is literally treated as thetask of putting m-features into proper setups and in proper se-quence, which is called m-sequencing in DPP. A high-level pro-cess plan as a result of m-sequencing only consists of machine-

neutral information in the form of generic machining sequences,including both critical (having datum references and manufac-turing constraints) and noncritical machining operations. Someof the noncritical ones are presented in a parallel order, whosesequence will be determined by a CNC controller during opera-tion planning. Before an m-feature can be machined, it must begrouped into a setup for the ease of fixturing. The basic idea offeature grouping is to determine a primary locating direction ofa setup and group the appropriate m-features into the setup ac-cording to their predefined tool access directions. This processis repeated for a secondary locating direction and so on until allthe m-features are properly grouped.

Here, a primary locating direction is the surface normal⇀

V ofthe primary locating surface (LS). It can be determined by thefollowing equations:

LS =

{f (A∗, T ∗)

∣∣∣∣∣WA × A∗

Amax+ WT × T ∗

Tmax

= max(

WA × A

Amax+ WT × T

Tmax

)}(1)

⇀

V =[∂f

∂x,∂f

∂y,∂f

∂z

](2)

where A∗ and T ∗ are the surface area and the generalized ac-curacy grade of an LS, WA is the weight factor of A∗, WT

is the weight factor of T ∗, and Amax and Tmax are the maxi-mum values of A∗ and T ∗ of all candidate locating surfaces. Ageneralized accuracy grade T can be obtained by applying thealgorithms described in [17]–[19]. The surface f(A∗, T ∗) sat-isfying the second half of (1) is chosen as the primary locatingsurface, whose surface normal can be derived by the derivationswith respect to x, y, and z, respectively. Based on the primarylocating direction

⇀

V , those m-features whose tool access direc-tions

⇀

TEMF are opposite to⇀

V are grouped into setup ST⇀V

asfollows:

ST⇀V

={EFM

∣∣∣⇀

TEMF = − ⇀

V}

. (3)

To be generic, the setups at this stage are planned for three-axis machines only. A setup merging is handled by the ExecutionControl module for four-axis or five-axis machines, if needed,after a specific CNC machine is selected.

In order to further sequence m-features in each setup, a geom-etry reasoning algorithm using intermediate machining volume(IMV) was proposed [20]. An IMV of an m-feature is the in-tersection of its maximum machining volume (MMV) and thecurrent workpiece. Fig. 4 schematically shows the concept ofIMV through a hole, where the IMV of the hole varies betweenits MMV and its actual machining volume (AMV) during themachining.

Based on the concept of IMV, five reasoning rules are definedfor m-sequencing.

Rule 1: If the IMV of an m-feature equals to the AMV of them-feature, or IMV = AMV, it is the time to machine them-feature.



Fig. 4. Intermediate machining volume of a hole feature. (a) A Hole in a part.(b) Current workpiece. (c) Final workpiece. (d) MMV of Hole. (e) IMV of Hole.(f) AMV of Hole.

Rule 2: If the IMV of m-feature A is to be divided into morethan one piece as a result of the machining operation ofm-feature B, m-feature A should be cut first.

Rule 3: If an m-feature is to be changed to another m-featuretype as a result of its own machining operation, this m-feature should be cut later.

Rule 4: A bigger machining volume is to be cut first.Rule 5: In a setup, the m-features sharing the same tool types

are grouped into clusters.

These five reasoning rules are used effectively for m-sequencing as demonstrated in the case study in Section VI.The sequenced m-features are then embedded in a set of FBswith built-in decision-making functions of cutting parametersselection, tool path generation, and G-code generation at theindividual m-feature level. The FBs are grouped in setups (orcomposite FBs) and are then dispatched to a selected machine,one setup at a time, where detailed operation planning is accom-plished before fabrication. The built-in functions of each FB areevent-driven, and can be triggered at run-time upon request todetermine an optimal set of cutting parameters and tool paths soas to adapt to any environmental changes. Details on FB designand its utilization are explained in the next section.

C. Function Block Design and Utilization

“Function blocks” (or IEC 61499-1) [21] are an IEC standardfor distributed process measurement and control, particularly forPLC control. An FB is a reusable functional module based onan explicit event-driven model, and provides for data flow andfinite-state automata-based control. It is relevant to CNC controlin machining data encapsulation and process plan execution. Inthe DPP, we use FBs to address the manufacturing uncertaintythrough resource-driven algorithms embedded in each FB. Theevent-driven model (or resource-driven algorithms) of an FBgives a CNC machine more intelligence and autonomy to makedecisions on how to adapt a generic process plan to match theactual machine capacity and dynamics. It also enables dynamictask scheduling, execution control, and process monitoring.

Three basic FB types are defined in the DPP: 1) machin-ing feature FB (MF-FB); 2) event switch FB (ES-FB); and 3)service interface FB (SI-FB). Fig. 5(a) depicts a typical 4-Side

Fig. 5. Basic machining feature FB. (a) Structure design of a 4-Side PocketMF-FB. (b) Execution control of embedded algorithms.

Pocket MF-FB. A basic FB like this can have multiple outputsand can maintain its unique internal state, meaning that it cangenerate different outputs even if the same inputs are applied.The fact is of vital importance for adaptive cutting conditionmodification, after the FB has been dispatched to a machine,by changing the internal hidden state of the FB. For example,the same 4-Side Pocket MF-FB can be used for roughing and/orfinishing at the same machine (or at a different machine) withdifferent cutting parameters and tool paths, by adjusting the in-ternal state of the FB to fine-tune the algorithms in use. Such abehavior is controlled by a finite-state machine, whose operationis represented by an execution control chart (ECC), as shownin Fig. 5(b).

The START state is an initial idle state ready for receivingevent inputs. EI_INI (an incoming event requesting initializa-tion) triggers the state transition from START to INI for FBinitialization, and when the state INI is active, the algorithmALG_INI is being executed for the initialization. Upon its com-pletion, ALG_INI will trigger an event output EO_INI indicat-ing the success of the initialization.

Similarly, for other state transitions to RUN, UPDATE, andMON (execution monitoring), different algorithms ALG_RUN(MF-FB execution), ALG_UPDATE (cutting condition update),and ALG_MON (MF-FB monitoring) are triggered, corre-spondingly. An event “1” means that a state transition is alwaystrue. That is to say, the state will transit back to the START stateand be ready for receiving the next event input.



Fig. 6. Event switch FB for parallel m-features sequencing. (a) An ES-FB ina composite function block. (b) Structure design of an ES-FB.

While basic MF-FBs define the functional relationships ofevents, data, and algorithms for individual machining featuresfabrication, their combination can form a composite FB repre-senting a setup. A composite FB may consist of several basicand/or composite FBs with partially sequenced connections viaevents and data. The event flow among MF-FBs determines theirmachining sequence. Fig. 6(a) shows a composite FB, where theevent flow (or sequence) among three MF-FBs is facilitated atruntime by an event switch FB (ES-FB). For instance, if a se-quence of “342” is given, the ES-FB will fire events accordinglyto appropriate MF-FBs for feature fabrications in the order of3 → 4 → 2. It thus adds flexibility to the composite FB. Fig. 6(b)illustrates the graphical definition of the ES-FB, where ROUTEis the only data input to the FB. It is used as a reserved port forcontroller-level operation planning to do the local optimizationof machining sequence.

Once the final sequence becomes explicit for those parallelMF-FBs, a string of integer numbers indicating the sequence isapplied to the port. Event switching is realized by the internalalgorithm ALG_SWITCH, which parses the data string andtriggers one execution event at a time until the entire string isexhausted.

In addition to MF-FBs and ES-FB, a service interface FB (SI-FB) is defined, as shown in Fig. 7(a), to facilitate the executioncontrol of MF-FBs. It also enables machining process monitor-ing during FB execution. In DPP, all MF-FBs are grouped insetups before being dispatched to appropriate machines. Eachsetup is a composite FB (CFB). An SI-FB is plugged to eachsetup with the following assigned duties: 1) collects run-time ex-ecution status of an MF-FB including FB id, cutting parameters,and job completion rate; 2) collects machining status (cutting

force, cutting heat, vibration, etc.) if made available; and 3) re-ports any unexpected situations to DPP, e.g., security alarm, toolbreakage, etc.

Similar to other FB types, an SI-FB has been designed withfive embedded algorithms for requesting and reporting execu-tion status (ES), machining status (MS), and unexpected situa-tion (US) from MF-FBs and to the Execution Control module(see Fig. 3), respectively. Fig. 7(b) is such an example. In orderto monitor the machining process during execution, an SI-FB isplugged to the CFB. Per the request from the Execution Controlmodule, the SI-FB will pass the request (EI_ESR, executionstatus request) to the CFB, which will then return an array ofFB_EXE containing runtime execution status back to the SI-FBand finally to the Execution Control module. The SI-FB is of vi-tal importance for machining process monitoring and dynamicrescheduling in case of machine failure.

The specific algorithms for each FB type are implementedin Java. More details on FB design and implementation will bereported separately.

In the DPP, there is a module dedicated to FB design (seeFig. 3). This FB designer consists of a basic FB designer, acomposite FB designer, and an FB network designer. As thename suggests, each FB designer performs a specific function.Fig. 8(a) illustrates a four-side pocket MF-FB being designedusing the basic FB designer, whereas Fig. 8(b) depicts the resultof a CFB (a setup). In the DPP, a set of sequenced machiningfeatures can be mapped to a network of CFBs easily using thisdesign tool. An example for a test part machining is explainedin detail in Section VI.

D. Shop Floor Integration

Enabled by the Wise-ShopFloor, true shop floor integrationcan be realized by combining the following three subsystems:1) the DPP subsystem; 2) an agent-based scheduling subsys-tem; and 3) a Web-based monitoring and control subsystem,where DPP is treated as the main thread (Fig. 9). The schedul-ing subsystem is relatively stand-alone, to which the integrationis loosely coupled. More details on dynamic scheduling can befound in [22].

In DPP, a generic process plan is embedded in a set of FBsthat are portable to different machines. The machine-specificdata, however, are determined at runtime by the FB-embeddedalgorithms that are adaptive to unpredictable situations. For ex-ample, an alternative resource (cutter or machine tool) has to beused due to tool shortage or machine breakdown. In this case,the FBs can apply appropriate algorithms to dynamically figureout the best cutting parameters and tool path for the alternativeresource without redoing the entire process planning. A snap-shot of the adaptive process planning and its integration withWeb-based remote machining is demonstrated in Section VIthrough a simple case study.

V. WEB-BASED REAL-TIME MONITORING AND CONTROL

Obtaining real-time monitoring, control, and inspection datafor a machine is limited by the available bandwidth for thedata transfer. Broadcasting data about all machines to all clients



Fig. 7. Service interface FB for execution control and monitoring. (a) Structure design of an SI-FB. (b) SI-FB for process monitoring.

would require sending more messages than necessary, slowingdown the transfer of each message, and reducing the applica-tion’s ability to display data and images in real time. Pollinginitiated by a client requires two-way communication, whileonly the information sent to the server from the client is of anyuse. The best solution to reducing network congestion and en-suring quick transfers is to have data multicast to only the clientsrequiring that data, with an open connection established for datastreaming, and sending data whenever the data are changed. Thissection presents in detail the system configuration, sensor datacollection and distribution, and Java 3D based visualization.

A. System Configuration

Fig. 10 illustrates a typical configuration for Web-based rapidmachining, where a five-axis horizontal milling machine ishooked up to the network for remote monitoring and CNC ma-chining. The milling machine is equipped with a PC-based openarchitecture controller that serves as a gateway between itself

and the application server. For security reason, TransmissionControl Protocol (TCP) has been adopted for data communica-tion between the machine and the application server, whereasHTTP streaming is used for data sharing from the server to theremote users. While the former is better for hardware protectionwith handshaking, the latter is firewall-transparent and suitablefor Web-based application. Based on this configuration, it al-lows a remote user to monitor the absolute and relative motionsof all axes as well as to control the spindle speed and feed ratefor CNC machining.

B. Sensor Data Collection for Real-Time Monitoring

To this end, the Wise-ShopFloor implements a publish–subscribe design pattern. A client (end user) subscribes to in-formation pertaining to a specific machine, leaving an openconnection to receive events. When a new event for that ma-chine is posted, it is published only to those clients who havesubscribed to it. In the Wise-ShopFloor, this communication is



Fig. 8. FB design in Wise-ShopFloor. (a) Designing a basic function block.(b) Designing a composite function block.

handled by a modification of the Pushlet [23]. Fig. 11 showsthe communication pathway for events to and from clients anda real machine.

The client-side applet of the Wise-ShopFloor communicateswith the Pushlet, an HTTP servlet. Invoking the Pushlet with anHTTP “Get” request with a “subject” parameter allows a client tosubscribe to that subject. When receiving a subscription request,the Pushlet leaves the connection to the client open, allowingdata to be streamed in without reopening a connection for eachevent. On the client side of the Pushlet package is the Java-PushletClient. The JavaPushletClient sends the request for thePushlet subscription, and opens an input stream from the socket.The Pushlet client then loops continuously and checks for datain the data stream. If the publisher has written new data to thestream, the Pushlet client overwrites the next most recent datawith the new data, ensuring that only the most recent informa-tion is used to update a Java 3D image. The actual update of theimage, however, comes from a different loop. Java 3D providesan interface, the InputDevice, which can be registered to the Java3D Physical Environment. Once registered, a schedule is createdto call a polling and processing method from the InputDevice.In the Wise-ShopFloor, this schedule is designed such that themethod is called each time a frame is rendered, so that each

Fig. 9. Shop floor integration enabled by Wise-ShopFloor.

Fig. 10. Configuration of Web-based rapid machining.

frame renders a machine with only the most recent informationabout the machine. Fig. 12 shows the pathway of applet–servletcommunication.

The Publisher sends information through the connection es-tablished by the Pushlet. These data are found by the JavaPush-letClient loop, and is pushed into a client-side storage location.On a different thread, the Java 3D rendering loop retrieves thedata and updates the on-screen image for monitoring.

The Pushlet also provides a Postlet servlet, used by clients to“Post” events to the Publisher. When a client wishes to control amachine, he/she needs to seek permission from the applicationserver and then enters into the control mode. At any given time,only one client can be granted the control authority for manip-ulating a given machine. The client-side applet then connectsto the Postlet, sending an HTTP “Get” request with the desired



Fig. 11. Event and data flow between clients and devices.

Fig. 12. Streaming-based applet-servlet communication.

Fig. 13. Streaming-based applet-servlet communication.

instructions as a parameter. When the Postlet passes the data tothe Publisher, the connection is closed, while the Publisher sendsthe data to all clients who subscribe to the indicated subject.

On the real machine side, data collection is slightly different.There are many different types of machines and robots thatusually have different types of controllers. The Pushlet packageprovides an adapter, the Event Pull Source (see Fig. 11), whichcan be extended to obtain data from a required source (realdevice). Events are “pulled” from an Event Pull Source at aregular interval, which can be set to a desired increment toapproximately replicate real-time monitoring. A comprehensivedata flow is shown in Fig. 13, where the needed sensory data aredirected to the right users using the HTTP streaming.

In the collection of sensor data from real machines, the servercontaining the Pushlet actually acts as a client of the machinecontrollers, establishing a socket connection and working withthe provided interface of each machine controller. The concreteimplementation of the Event Pull Source is one adapter betweenthe interface of a machine controller and the interface requiredby the Pushlet. However, the communication to the real machine

must be in both directions to achieve control, although the EventPull Source communicates in only a single direction—from themachine to the application server. A machine controller is notable to interpret the Pushlet event, and thus, will not be a clientof the Pushlet. Another Pushlet adapter, the Machine Adaptor,is required to take information from the Postlet (i.e., from theclient), and send it to a machine controller in the required format.As the Pushlet does not provide this functionality, the Wise-ShopFloor uses a wrapper for the Postlet, which determineswhether data are destined for the publisher or the machine, andthus, directs it appropriately.

C. Data Packet Format

As shown in Fig. 10, run-time sensory data collection fromthe milling machine is accomplished over the TCP connectionusing a series of 12 floating numbers and one long integer thatform one data packet. In the current implementation, a typicaldata packet is defined as follows:

where FR, SS, and CW denote feed rate, spindle speed, andNC control word, respectively. A control word is a reservedlong integer indicating the status of the machine, includingoperation mode, such as manual (0x0001), auto (0x0002), orjogging (0x0040), coordinate system, axis status, etc. Sim-ilar to the real machine controller, the data packet pro-vides both relative and absolute positions of the five mo-tion axes that are used for joints transformation and Java 3Dmodel rendering for the ease of off-site monitoring and CNCcontrol.

D. Java-3D-Enabled Visualization

For the sake of network bandwidth conservation, Java 3Dis chosen for geometric modeling of the CNC machine, as analternative of camera-based solutions. Java 3D is designed tobe a fourth-generation 3-D application programming interface(API) [24]. What sets a fourth-generation API apart from itspredecessors is the use of scene-graph architecture for organiz-ing 3-D objects in the virtual world. Enabled by the scene-grapharchitecture, Java 3D provides an abstract, interactive imag-ing model for behavior control of 3-D objects. Different fromother scene-graph-based systems, a Java 3D scene graph is a di-rected acyclic graph. The individual connections between Java3D nodes are always forming a direct relationship: parent tochild.

The five-axis milling machine shown in Fig. 10 requires lin-ear motion control of X-, Y -, and Z-axes, as well as rotarymotion control of B and C (around Y - and Z-axes, respec-tively). A combined rotary stage having two rotary motions ismounted on top of an X-table, whereas the spindle head of themachine provides the other two linear motions along Y - andZ-axes. Fig. 14 illustrates the Java 3D scene graph model of themachine.



Fig. 14. Java 3D scene graph model of a five-axis milling machine.

The scene graph contains a complete description of the en-tire scene. It includes the geometries, the attributes, and theviewing information needed to render the scene from a par-ticular point of view. All Java 3D scene graphs must connectto a Virtual Universe object to be displayed. The Virtual Uni-verse object provides grounding for the entire scene. A scenegraph itself, however, starts with BranchGroup (BG) nodes (al-though only one BG node in this case). A BG node serves asthe root of a subgraph, or a branch graph, of the scene graph.The TransformGroup nodes inside a branch graph specify theposition, the orientation, and the scale of the geometric ob-jects in the virtual universe. Each geometric object consists ofa Geometry object, an Appearance object, or both. The Ge-ometry object describes the geometric shape of a 3-D object.The Appearance object describes the appearance of the ge-ometry (color, texture, material reflection characteristics, etc.).The behavior of the machine is controlled by Behavior nodes,which is subject to sensor data and is implementation-specific.The results of sensor data processing can be embedded intothe codes for remote monitoring. Once applied to a Trans-formGroup node, the so-defined behavior control affects allthe descending nodes. In our case, the five-axis motions (X-table, rotary stage 1, rotary stage 2, spindle head, and spindle)are controlled by their corresponding behavior control nodes,for both on-line monitoring/control and offline simulation. Asthe Java 3D model is connected with its physical counterpartthrough the control nodes by low-volume message passing (real-time sensor signals and control commands), it becomes possi-ble to remotely machine a part on the real machine throughthe Wise-ShopFloor, where the physical security is addressedseparately.

E. Web-Based Remote CNC Control

Web-based CNC control is possible by sending proper NCcommands through the applet–servlet (or CyberController–ControlCommander–Machine) communication, as shown inFig. 2. In order to remotely machine a part, user authentica-tion and authorization must be accomplished for the client who

demands this operation. Control right authorization is done bysetting a bit in the control word in a data packet that is sentto the client. If the client has requested the control right andthe bit is set, a message will appear on the screen notifying theuser that he/she is now in control of the machine. For the pur-pose of remote machining, a control word, similar to CW in themonitoring data packet, is sent back to the machine controller,augmented by a text string containing lines of an NC program.Thus, not just manual control can be exercised off-site, but acomplete NC program generated by the DPP can be remotelyexecuted. For example, the following NC line tells the machinecontroller to proceed from the current position to the next, incre-mentally by (20, −30, 10) in linear rapid traverse mode. At thesame time, the controller sets the spindle speed to 3000 r/minand turns the flood coolant on

G0 X+20 Y−30 Z+10 S3000 M8

Most existing Web-based systems rely on camera-based mon-itoring to guide remote operations. Compared with one 8-bit video graphics array (VGA) camera image of 640 × 480(307 200 bytes), the data packet size of Wise-ShopFloor is only52 bytes—a significant size reduction suitable for Web-basedreal-time applications.

VI. CASE STUDY

A test part shown in Fig. 15(a) is chosen for the case study.After applying the five feature-based reasoning rules definedin Section IV, the 14 m-features are grouped into two setups,each of which consists of two or more partially sequenced m-features, as shown in Fig. 15(b). While each m-feature can bemapped to a FB, a setup forms a composite FB. Fig. 16 showsthe composite FB for setup 2, where the needed NC code canbe generated at run-time by the MF-FBs.

In the Wise-ShopFloor, the adaptive process plan shown inFig. 16 can be dispatched to a milling machine for rapid fabri-cation utilizing the real-time monitoring and control functionsdiscussed in Section V. The motions of the five axes of thismachine are driven by either sensor data for client-side moni-toring or user commands for remote control. As the 3-D modelis connected with its physical counterpart through the messagepassing, it becomes possible to remotely manipulate the realmachine through its Java 3D model. For example, the joggingcontrol is with the use of the individual control buttons, as la-beled in Fig. 17, whereas feature machining can be remotelyachieved through NC control mode.

The data packet format and the current implementation pro-vide all information needed by the Java 3D model and its physi-cal counterpart, the milling machine, for process plan execution.The 3-D model ignores the first five numbers, while the machinecontroller ignores the second five numbers.

As mentioned in Section III, although the Wise-ShopFloorprovides an alternative of camera-based monitoring, an off-the-shelf Web-ready camera can easily be switched on remotelyto capture unmodeled scenes for troubleshooting. Fig. 18 illus-trates one snapshot of a real scene of CNC machining duringthe case study.



Fig. 15. Test part and its machining sequence. (a) Test part design. (b) Sequenced m-features.

Fig. 16. Adaptive process plan embedded in a composite FB.

Fig. 17. User interface for Web-based remote machining.

The Wise-ShopFloor prototype system provides users with aWeb-based collaborative environment for real-time monitoringand control of manufacturing devices in the shop floor. It utilizes

Fig. 18. Snapshot of Web-based remote machining.

the latest technologies, including Java 3D and Servlets, for sys-tem design and implementation. Fig. 19 shows the modular userinterfaces that form the integrated system.



Fig. 19. Integrated system for collaborative manufacturing.

VII. CONCLUSION

This paper presents a novel approach toward Web-based col-laborative manufacturing, including distributed process plan-ning, dynamic scheduling, real-time monitoring, and remotecontrol. On top of a Wise-ShopFloor framework, a prototypesystem has been designed into VCM architecture and devel-oped using publish–subscribe design pattern for sensor datacollection and distribution. In terms of process planning, ourapproach is to separate machine-specific data from generic onesusing two-layer supervisory planning and operation planning.A generic process plan has been embedded into FBs with built-in algorithms for machine-level adaptive decision-making. Aplanning–machining case study demonstrates its feasibility andshows promise of this approach in a distributed manufacturingenvironment.

The major contributions of this research include: 1) a Wise-ShopFloor framework for collaborative manufacturing; 2) a DPPapproach for adaptive process planning; 3) a sensor-driven ap-proach for Web-based real-time monitoring and control; and4) a dynamic scheduling approach (to be reported separately)based on real-time monitoring information. As decentralizationof business grows, a large application potential of this researchis anticipated, such as control simulation, operator training,facility touring, off-site troubleshooting, and collaborative de-sign verification, in addition to remote real-time monitoring andcontrol.

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Lihui Wang received the B.Sc. degree in machine de-sign from the Academy of Arts and Design (now partof Tsinghua University), Beijing, China, in 1982, andthe M.E.Sc. and Ph.D. degrees in mechanical engi-neering from Kobe University, Kobe, Japan, in 1990and 1993, respectively.

He is currently a Senior Research Officer withthe Integrated Manufacturing Technologies Institute,National Research Council of Canada, London, ON.He is the author and editor of three books on con-dition monitoring and control, process planning and

scheduling, and smart devices and machines. He has also edited ten conferenceproceedings and journal special issues in design and manufacturing. His currentresearch interests include distributed process planning, Web-based machining,remote real-time monitoring and control, collaborative design, and intelligentmanufacturing systems. He has authored or coauthored more than 180 bookchapters, archival journal papers, and peer-reviewed conference articles. He isthe Editor-in-Chief of the International Journal of Manufacturing Research,the Editor of Robotics and Computer Integrated Manufacturing, the RegionalEditor of Journal of Intelligent Manufacturing, an Editorial Board member ofother five international journals, and an Adjunct Professor at the University ofWestern Ontario, Canada.

Dr. Wang is a Registered Professional Engineer.


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