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  • 251.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Two-Stage Setup Planning for Job-Shop Machining Operations2011In: Proceedings of the 4th International Swedish Production Symposium, Swedish Production Academy , 2011, p. 141-147Conference paper (Refereed)
  • 252.
    Wang, Lihui
    National Research Council of Canada.
    Using Function Blocks for Distributed Process Planning and Control2001In: Proceedings of ASME International Mechanical Engineering Congress and Exposition, 2001Conference paper (Refereed)
  • 253.
    Wang, Lihui
    National Research Council of Canada.
    Web-based Decision Making for Collaborative Manufacturing2009In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 22, no 4, p. 334-344Article in journal (Refereed)
    Abstract [en]

    This paper presents methodologies of web-based decision making for collaborative manufacturing, including web-based knowledge sharing, distributed process planning, dynamic scheduling, real-time monitoring and remote control, targeting distributed yet collaborative manufacturing environments. The web-based decision making is enabled by a framework that allows users to plan and control manufacturing operations based on information either gathered via the Web or collected from manufacturing devices. The objective of this research is to develop an integrated system for web-based collaborative planning and control, supported by real-time monitoring for dynamic scheduling. Details on the principle of the framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of remote machining is chosen as a case study to demonstrate the effectiveness of this framework toward web-based collaborative manufacturing.

  • 254.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wise-ShopFloor: A Portal Toward Collaborative Manufacturing2007In: Collaborative Product Design and Manufacturing Methodologies and Applications, Springer London, 2007, p. 151-174Chapter in book (Refereed)
  • 255. Wang, Lihui
    Wise-ShopFloor: An Integrated Approach for Web-based Collaborative Manufacturing2008In: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, E-ISSN 1558-2442, Vol. 38, no 4, p. 562-573Article in journal (Refereed)
  • 256.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Wise-ShopFloor: Linking Virtual Manufacturing to Real Production2009In: Proceedings of the 3rd International Swedish Production Symposium, 2009, p. 164-169Conference paper (Refereed)
    Abstract [en]

    This paper presents an integrated approach for web-based sensor-driven real-time monitoring and control. it is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. The objective of this research is to develop methodology and algorithms that utilise virtual manufacturing technology for real production. Details on the principle of the Wise-ShopFloor framework, system architecture, and a proof-of-concept prototype are reported in this paper. Remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based manufacturing.

  • 257.
    Wang, Lihui
    et al.
    University of Skovde.
    Adamson, Göran
    University of Skovde.
    Holm, Magnus
    University of Skovde.
    Moore, Philip
    De Montfort University.
    A review of function blocks for process planning and control of manufacturing equipment2012In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 31, no 3, p. 269-279Article in journal (Refereed)
    Abstract [en]

    Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods. based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment

  • 258.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Adamson, Göran
    Holm, Magnus
    Moore, Philip
    Current Status of Function Blocks for Process Planning and Execution Control of Manufacturing Equipment2011In: Proceedings of the 21st International Conference on Flexible Automation and Intelligent Manufacturing: FAIM 2011, Feng Chia University , 2011, Vol. 2, p. 963-973Conference paper (Refereed)
    Abstract [en]

    Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch size, requiring real-time monitoring for dynamic distributed decision making, as well as dynamic control capabilities that are able to handle, in a responsive and adaptive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well a machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.

  • 259. Wang, Lihui
    et al.
    Balasubramanian, S.
    Norrie, Douglas H.
    Agent-based Intelligent Control System Design for Real-time Distributed Manufacturing Environments1998In: Working Notes of the Agent Based Manufacturing Workshop, 1998, p. 152-159Conference paper (Refereed)
  • 260.
    Wang, Lihui
    et al.
    Calgary University, Canada.
    Balasubramanian, S.
    Norrie, Douglas H.
    Brennan, R.W.
    Agent-based Control System for Next Generation Manufacturing1998In: Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 1998, p. 78-83Conference paper (Refereed)
    Abstract [en]

    Future manufacturing systems will be required to be agile, flexible and fault-tolerant. The next-generation intelligent manufacturing systems will be multi-agent systems containing distributed control and application entities that dynamically collaborate to satisfy both local and global objectives. This paper focuses on the development of a generic control system design based on IEC-1499 function block standards, for real-time distributed manufacturing environments. The paper first describes the related research into the control system architecture using multi-agent cooperation, then reports on the intelligent controller design interface and automatic control code generation.

  • 261.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Bi, Z.M.
    Challenges for Better Sustainable Manufacturing2013In: Advances in Sustainable and Competitive Manufacturing Systems: 23rd International Conference on Flexible Automation & Intelligent Manufacturing, Springer, 2013, p. 1209-1222Conference paper (Refereed)
  • 262.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Bi, Z.M.
    Reconfigurable Assembly Systems – The State of the Art2006In: Proceedings of the 7th International Conference on Frontiers of Design and Manufacturing, 2006, Vol. 3, p. 33-40Conference paper (Refereed)
  • 263.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Bordatchev, E.
    Tsay, S.-M.
    Lang, S.
    CD-ROM Proceedings of 2002 NRC-NSC Canada-Taiwan Joint Workshop on Advanced Manufacturing Technologies2002Book (Refereed)
  • 264. Wang, Lihui
    et al.
    Bordatchev, E.
    Tsay, S.-M.
    Lang, S.
    Proceedings of 2002 NRC-NSC Canada-Taiwan Joint Workshop on Advanced Manufacturing Technologies2002Book (Refereed)
  • 265.
    Wang, Lihui
    et al.
    Integrated Manufacturing Technologies Institute, National Research Council Canada.
    Brennan, R.W.
    Balasubramanian, S.
    Norrie, D.H.
    Realizing Holonic Control with Function Blocks2001In: Integrated Computer-Aided Engineering, ISSN 1069-2509, E-ISSN 1875-8835, Vol. 8, no 1, p. 81-93Article in journal (Refereed)
    Abstract [en]

    Future manufacturing systems will be required to be agile, flexible and fault-tolerant. Development of these next-generation systems will not only require new approaches, but also new ways of thinking about these systems. This paper describes a real-time control architecture for distributed intelligent control that combines the emerging IEC-1499 standard for distributed control with the holonic control paradigm. The focus of the paper is on a generic control system designer for the lower process/machine control layer of this real-time control architecture. This paper outlines related research into control system architecture using multi-agent negotiation and cooperation, then describes the intelligent control function design interface and automatic control code generation.

  • 266.
    Wang, Lihui
    et al.
    Integrated Manufacturing Technologies Institute, National Research Council of Canada.
    Cai, N.
    Feng, H.-Y.
    Liu, Z.
    Enriched Machining Feature Based Reasoning for Generic Machining Process Sequencing2006In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 44, no 8, p. 1479-1501Article in journal (Refereed)
    Abstract [en]

    This paper presents an enriched machining feature (EMF)-based reasoning approach to generic machining process sequencing for distributed process planning (DPP). An EMF is represented by combining its machining volume with surface, geometric and volume features, as well as other technological information needed to machine the feature. The information embedded in the EMF is retrieved progressively for machining sequence generation. Following an introduction of EMF and its representation scheme, the problems in determining machine-independent feature groups (set-ups) in DPP and their machining sequences to be followed for a given part are investigated. Based on the EMF concept, five reasoning rules are formulated and the algorithms developed. As the set-ups and sequences are generated based on manufacturing constraints and datum references but separated from specific resources, they are generic and applicable to machine tools with varying configurations and capabilities. This approach is further validated through a case study.

  • 267.
    Wang, Lihui
    et al.
    National Research Council of Canada .
    Cai, Ningxu
    Feng, Hsi-Yung
    Dynamic Setup Dispatching and Execution Monitoring Using Function Blocks2007In: Proceedings of the 2nd CIRP International Conference on Changeable, Agile, Reconfigurable and Virtual Production, 2007, p. 699-708Conference paper (Refereed)
  • 268.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Cai, Ningxu
    Feng, Hsi-Yung
    Function Blocks Enabled Dynamic Setup Dispatching and Execution Monitoring2009In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 22, no 1, p. 3-12Article in journal (Refereed)
  • 269.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Cai, Ningxu
    Feng, Hsi-Yung
    Generic Machining Sequence Generation Using Enriched Machining Features2004In: Transactions of the North American Manufacturing Research Institution of SME, ISSN 1047-3025, Vol. 32, p. 55-62Article in journal (Refereed)
    Abstract [en]

    The manufacturing processes in today's decentralized manufacturing systems are rather complex, especially at shop floors where highly mixed products in small batch sizes are handled simultaneously. In addition to the fluctuating job shop operations, unpredictable issues like job delay, urgent job insertion, fixture shortage, missing tool, and even machine break-down, are regularly challenging the job shop operations. Targeting the fluctuation, this research proposes a DPP (distributed process planning) approach to generate process plans that are responsive and adaptive to the changes. In this paper, a concept of enriched machining feature is introduced, based on which several rules and algorithms are formulated for generic machining sequence generation. It is expected that the new approach can largely enhance the dynamism of fluctuating job shop operations.

  • 270.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Cai, Ningxu
    Feng, Hsi-Yung
    Overview of a Distributed Process Planning Approach Targeting Manufacturing Uncertainty2006In: Proceedings of the International Conference on Manufacturing Science and Engineering, 2006, p. 595-604Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of our DPP (distributed process planning) approach, covering DPP concept, generic machining process sequencing using enriched machining features, process plan encapsulation in function blocks, and process monitoring enabled by the function blocks. A two-layer structure of Supervisory Planning and Operation Planning is proposed in DPP to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at shop level, whereas the operation planning is carried out at runtime at machine level. This dynamic decision-making is facilitated by a set of resource-driven algorithms embedded in the function blocks. The internal structures of typical function blocks are also introduced in the paper. The DPP approach and algorithms are further verified through a case study before drawing conclusions. It is expected that the new approach can largely enhance the dynamism of fluctuating job shop operations.

  • 271.
    Wang, Lihui
    et al.
    National Research Council of Canada .
    Cai, Ningxu
    Feng, Hsi-Yung
    Toward Adaptive Job Shop Planning in Dynamic Environment2005Conference paper (Refereed)
  • 272.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Cai, Ningxu
    Feng, Hsi-Yung
    Ma, Ji
    ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments2010In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 7, no 1, p. 2-14Article in journal (Refereed)
    Abstract [en]

    This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.

  • 273.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Cai, Ningxu
    Feng, Hsi-Yung
    Shen, Weiming
    Feature-Based Reasoning for Machining Process Sequencing in Distributed Process Planning2003In: Proceedings of the 13th International Conference on Flexible Automation and Intelligent Manufacturing, 2003, Vol. 2, p. 655-667Conference paper (Refereed)
  • 274.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Feng, Hsi-Yung
    Adaptive Manufacturing2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 3, p. 117-117Article in journal (Other academic)
  • 275.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, Hsi-Yung
    An Architecture for Distributed Process Planning Using Function Blocks2002In: Transactions of the North American Manufacturing Research Institution of SME, ISSN 1047-3025, Vol. 30, p. 613-620Article in journal (Refereed)
    Abstract [en]

    Today's machining shop floors, characterized by large variety of products in small batch sizes, require dynamic process planning capabilities that are responsive and adaptive to the rapid changes of production capacity and functionality. To meet the requirement, this research proposes a new methodology for dynamic and distributed process planning. The primary focus of this paper is on the architecture of a new approach using function blocks. The secondary focus is given to the other supporting technologies - machining features and agents. Different from conventional methods, this approach uses a two-layer structure - supervisory planning and operation planning. It is expected that the new architecture can improve the system performance for a dynamic environment.

  • 276.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, Hsi-Yung
    Cai, Ningxu
    Jin, Wei
    An Effective Approach for Distributed Process Planning Enabled by Event-driven Function Blocks2007In: Process Planning and Scheduling for Distributed Manufacturing, Springer London, 2007, p. 1-30Chapter in book (Refereed)
    Abstract [en]

    This chapter presents a function block enabled approach towards distributed process planning. It covers the basic concept, generic machining process sequencing using enriched machining features, process plan encapsulation in function blocks, and process monitoring through event-driven function blocks. A two-layer structure of supervisory planning and operation planning is proposed to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at the shop level to generate machine-neutral process plans, whereas the operation planning is carried out at runtime at the machine level to determine machine-specific operations. This dynamic decision making is facilitated by resource-driven algorithms embedded in the function blocks. The internal structures of typical function blocks are also introduced in the chapter. Our approach and algorithms are verified through case studies before drawing conclusions. It is expected that the new approach can greatly enhance the dynamism of fluctuating job-shop operations.

  • 277.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Feng, Hsi-Yung
    Cai, Ningxu
    Ma, Ji
    Adaptive Setup Planning for Job Shop Operations under Uncertainty2009In: Collaborative Design and Planning for Digital Manufacturing, Springer London, 2009, p. 187-216Chapter in book (Refereed)
  • 278.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, Hsi-Yung
    Song, Changjin
    Jin, Wei
    Function Block Design for Adaptive Execution Control of Job Shop Machining Operations2009In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 47, no 12, p. 3413-3434Article in journal (Refereed)
    Abstract [en]

    Small volume and high product mix contribute greatly to the complexity of job shop operations. In addition, shop floor uncertainty or fluctuation is another issue regularly challenging manufacturing companies, including job delay, urgent job insertion, fixture shortage, missing tools, and even machine breakdown. Targeting the uncertainty, we propose a function block-based approach to generating adaptive process plans. Enabled by the function blocks, a so-generated process plan is responsive and tolerant to an unpredictable change. This paper presents in detail how a function block is designed and what it can do during process plan execution. It is expected that this new approach can largely enhance the dynamism of fluctuating job shop operations.

  • 279.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, H.-Y.
    Cai, N.
    Architecture Design for Distributed Process Planning2003In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 22, no 2, p. 99-115Article in journal (Refereed)
    Abstract [en]

    Today's machining shop floors, characterized by a large variety of products in small batch sizes, require dynamic process planning capabilities that are responsive and adaptive to the rapid changes of production capacity and functionality. To meet the requirement, this research proposes a new methodology for dynamic and distributed process planning. The primary focus of this paper is on the architecture of a new approach using function blocks. The secondary focus is given to the other supporting technologies-machining features and agents. Different from conventional methods, this approach uses a two-layer structure-supervisory planning and operation planning. It is expected that the new architecture can improve the system performance in a dynamic environment. 

  • 280.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, H.-Y.
    Song, Changjin
    Jin, Wei
    Function Block Design to Enable Adaptive Job Shop Operations2007Conference paper (Refereed)
    Abstract [en]

    Small volume and high product-mix contribute greatly to the complexity of job shop operations. In addition, shop floor uncertainty or fluctuation is another issue regularly challenging manufacturing companies, including job delay, urgent job insertion, fixture shortage, missing tool, and even machine breakdown. Targeting the uncertainty, we propose a function block based approach to generating adaptive process plans. Enabled by the function blocks, a so-generated process plan is responsive and tolerant to an unpredictable change. This paper presents in detail how a function block is designed and what it can do during process plan execution. It is expected that this new approach can largely enhance the dynamism of fluctuating job shop operations.

  • 281.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
    Preface2017In: Procedia Manufacturing, ISSN 2351-9789, Vol. 10, p. vii-viiiArticle in journal (Refereed)
  • 282.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
    Preface2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 26, p. 1-2Article in journal (Refereed)
  • 283.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
    Special Issue of Journal of Manufacturing Processes on Advancing Manufacturing Processes Research at NAMRC 462018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 26, p. 8-9Article in journal (Refereed)
  • 284.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Fratini, L.
    Italy.
    Shih, A. J.
    United States.
    Special Issue of Journal of Manufacturing Systems on Advancing Manufacturing Systems Research at NAMRC 462018In: 46th SME North American Manufacturing Research Conference, NAMRC 2018, Elsevier, 2018, Vol. 26, p. 6-7Conference paper (Refereed)
  • 285.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Fratini, Livan
    Univ Palermo, Palermo, Italy..
    Shih, Albert J.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Advancing manufacturing processes research at NAMRC 462018In: JOURNAL OF MANUFACTURING PROCESSES, ISSN 1526-6125, Vol. 34, p. 733-733Article in journal (Other academic)
  • 286.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Fratini, Livan
    Univ Palermo, Palermo, Italy..
    Shih, Albert J.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Advancing manufacturing systems research at NAMRC 462018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 1-2Article in journal (Other academic)
  • 287.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. NAMRI/SME Scientific Committee Chair, KTH Royal Institute of Technology, Stockholm, Sweden.
    Fratini, Livan
    Shih, Albert J.
    Latest advancements in manufacturing systems at NAMRC 452017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 44, p. 271-272Article in journal (Other academic)
  • 288.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, Livan
    Shih, Albert J.
    Latest developments in manufacturing processes at NAMRC 452017In: JOURNAL OF MANUFACTURING PROCESSES, ISSN 1526-6125, Vol. 28, p. 411-412Article in journal (Other academic)
  • 289.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Gao, R.
    Ragai, I.
    An integrated cyber-physical system for cloud manufacturing2014In: ASME 2014 International Manufacturing Science and Engineering Conference, ASME Press, 2014, Vol. 1Conference paper (Refereed)
    Abstract [en]

    This paper presents an integrated cyber-physical system for remote accessibility and controllability of factory equipment, e.g. CNC machines and robots. It is enabled by combining 3D models, sensor data and camera images in real-time. The aim of this research is to significantly reduce network traffic for much improved accessibility and controllability of any cyber-physical systems over the Internet. The ultimate goal is to build cloudbased services of monitoring, process planning, machining and assembly in decentralised environment. This paper covers the basis of the approach, system architecture and implementation, and a case study of remote control of a robotic assembly cell. Compared with camera-based systems, our approach consumes less than 1% of its network bandwidth, feasible and practical as a future cloud-based solution.

  • 290.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Gao, Robert X.
    Condition Monitoring and Control for Intelligent Manufacturing2006Collection (editor) (Refereed)
  • 291.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Gao, Robert X.
    Preface2006In: Condition Monitoring and Control for Intelligent Manufacturing, Springer London, 2006, p. v-viiChapter in book (Refereed)
  • 292.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Givehchi, M.
    Adamson, G.
    Holm, M.
    A Sensor-Driven 3D Model-Based Approach to Remote Real-Time Monitoring2011In: CIRP Annals – Manufacturing Technology, ISSN 0007-8506, Vol. 60, no 1, p. 493-496Article in journal (Refereed)
    Abstract [en]

    This paper presents an integrated approach for remote real-time monitoring of manufacturing operations. It is enabled by using virtual 3D models driven by real sensor data. The objectives of this research are twofold: (1) to significantly reduce network traffic for real-time monitoring over the Internet; and (2) to increase the controllability of manufacturing systems from anywhere in a decentralised environment. Particularly, this paper covers the principle of the approach, system architecture, prototype implementation, and a case study of remote control of a robotic assembly cell. Compared with camera-based monitoring systems, our approach only consumes less than 1% of its network bandwidth, feasible and practical as a web-based portable solution.

  • 293.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Högskolan i Skövde, Institutionen för teknik och samhälle, Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Givehchi, Mohammad
    Web-DPP: An Adaptive Approach to Planning and Monitoring of Job-Shop Machining Operations2011In: Proceedings of the 7th CIRP International Conference on Digital Enterprise Technology, 2011, p. 411-420Conference paper (Refereed)
    Abstract [en]

    Utilising the existing IT infrastructure, the objective of this research is to develop an integrated Web-based distributed process planning system (Web-DPP) for job-shop machining operations and their runtime execution monitoring. Our approach tries to engage a dispersed working group in a collaborative environment, allowing the team members to share real-time information through the Web-DPP. This paper analyses the challenges, and presents both the system design specification and the latest development of the Web-DPP system. Particularly, it proposes a two-tier architecture for effective decision making and introduces a set of event-driven function blocks for bridging the gap between high-level planning and low-level execution functions. By connecting to a Wise-ShopFloor framework, it enables real-time execution monitoring during the machining operations, locally or remotely. The closed-loop information flow makes adaptive planning possible.

  • 294.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Givehchi, Mohammad
    Schmidt, Bernard
    Adamson, Göran
    A Function Block Enabled Robotic Assembly Planning and Control System with Enhanced Adaptability2012In: Proceedings of the 45th CIRP Conference on Manufacturing Systems, 2012, p. 194-201Conference paper (Refereed)
  • 295.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    Combined strength of holons, agents and function blocks in cyber-physical systems2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 40, p. 25-34Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach to implementing cyber-physical systems (CPS) using the combined strength of holons, agents and function blocks. Within the context, a CPS is represented by a holarchy of multiple holons. Each holon possesses a logical part and a physical part, which mimic the cyber and physical entities of the CPS. During implementation, the two parts of a holon are realised by agents and function blocks for information processing and materials processing, respectively. The objective of this research is to provide a concept map and associate a CPS with holons, agents and function blocks for the ease of system implementation in decentralised or cloud environment.

  • 296.
    Wang, Lihui
    et al.
    Integrated Manufacturing Technologies Institute, National Research Council of Canada .
    Hao, Qi
    Shen, Weiming
    A Novel Function Block Based Integration Approach to Process Planning and Scheduling with Execution Control2007In: International Journal of Manufacturing Technology and Management (IJMTM), ISSN 1368-2148, E-ISSN 1741-5195, Vol. 11, no 2, p. 228-250Article in journal (Refereed)
    Abstract [en]

    In today's decentralised business environment, manufacturing enterprises are implementing advanced distributed manufacturing planning and control strategies to adapt to and win the fluctuating global market. Within the context, this paper presents a novel approach to the integration of process planning, scheduling and execution in dynamic machining shop floors. Based on the concept of distributed process planning (DPP), function blocks are adopted as information carriers to optimise and specialise the nonlinear process plans (NLPP) progressively throughout the three planning stages: supervisory planning, execution control and operation planning, with scheduling functionality integrated seamlessly. Architecture of DPP and function block based integration are proposed, followed by a system functional design using IDEF0 methodology. The advantages of adopting function block technology are demonstrated and recent research results as well as future directions are pointed out in this paper.

  • 297.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Hao, Qi
    Shen, Weiming
    Function Block Based Integration of Process Planning, Scheduling and Execution for RMS2003In: Proceedings of CIRP 2nd International Conference on Reconfigurable Manufacturing, 2003Conference paper (Refereed)
  • 298.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Holm, M.
    Adamson, G.
    Embedding a Process Plan in Function Blocks for Adaptive Machining2010In: CIRP Annals – Manufacturing Technology, ISSN 0007-8506, Vol. 59, no 1, p. 433-436Article in journal (Refereed)
    Abstract [en]

    This paper presents a function block enabled approach towards adaptive process planning and machining. A two-layer structure of supervisory planning and operation planning is proposed to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at the shop level to generate machine-neutral process plans, whereas the operation planning is carried out at runtime at the machine level to determine machine-specific operations. Such adaptive decision making is facilitated by event-driven algorithms embedded in the function blocks. It is expected that the new approach can greatly enhance the dynamism of fluctuating job-shop machining operations.

  • 299.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cloud enabled CPS and big data in manufacturing2018In: Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing, Pleiades Publishing , 2018, no 9783319895628, p. 265-292Chapter in book (Refereed)
    Abstract [en]

    This paper presents a cloud enhanced cyber-physical system (cloud CPS) in manufacturing by combining CPS and cloud technologies. The cloud CPS is enhanced by using the combined strength of holons, agents and function blocks (FBs). Here, a holarchy of multiple holons is a sub-CPS within cloud CPS, and a logical part and a physical part are involved in each holon, and they mimic the cyber and physical entities of the CPS. They are able to be realised by agents and FBs for the manufacturing processes. In addition, to address the weakness in operation level, big data analytics (BDA) is applied to optimise machining jobs and to predict faults in scheduling. Within the processes, machining relevant factors, including workpiece, machining requirement, machine tools, cutting tool, cutting conditions, machining process and machining results, are represented by data, which is able to solve the many operational issues in cloud CPS.

  • 300. Wang, Lihui
    et al.
    Jin, W
    Feng, HY
    Embedding machining features in function blocks for distributed process planning2006In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 19, no 5, p. 443-452Article in journal (Refereed)
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