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  • 251.
    Wang, Lihui
    National Research Council of Canada.
    Distributed Process Planning and Web-based Rapid Machining2007In: Proceedings of the 9th Conference on Machining and Advanced Manufacturing Technology, 2007, p. 24-41Conference paper (Refereed)
  • 252.
    Wang, Lihui
    National Research Council of Canada.
    DPP: A Distributed Process Planning Approach Using Function Blocks2002Conference paper (Refereed)
    Abstract [en]

    The objective of this research is to develop a new methodology for intelligent and distributed process planning. The primary focus of this paper is on the architecture of the new process planning approach, using function blocks as controller language. The secondary focus is on the other supporting technologies such as machining feature-based design and agent-based decision-making. The methodology proposed for distributed process planning is based on a design-for-machining concept that can seamlessly integrate feature-based design and agent-based planning into function block-based CNC control. Different from traditional methods, the proposed approach has a two-layer structure – supervisory planning and operation planning. The supervisory planning is performed in advance at shop floor level, followed by the operation planning accomplished at run-time at machine level by open CNC controllers. Through decentralization, the distributed process planning shows promise of improving system performance within today’s continually changing shop floor environment.

  • 253.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Dynamic Thermal Analysis of Machines in Running State2013Book (Refereed)
  • 254.
    Wang, Lihui
    National Research Council of Canada.
    Editorial of Inaugural Issue of IJMR2006In: International Journal of Manufacturing Research, Vol. 1, no 1Article in journal (Refereed)
  • 255. Wang, Lihui
    Innovative Technologies for Manufacturing2006In: International Journal of Manufacturing Technology and Management (IJMTM), ISSN 1368-2148, E-ISSN 1741-5195, Vol. 9, no 3-4, p. 201-203-Article in journal (Refereed)
  • 256.
    Wang, Lihui
    National Research Council of Canada, Integrated Manufacturing Technologies Institute.
    Integrated Design-to-Control Approach for Holonic Manufacturing Systems2001In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 8, no 1, p. 81-93Article in journal (Refereed)
    Abstract [en]

    Next generation manufacturing systems will be integrated networks of distributed resources simultaneously capable of combined knowledge and material processing. These manufacturing systems will be required to be agile, flexible, and fault-tolerant. The objective of this research is to define a generic open architecture for such kind of distributed manufacturing systems, especially for holonic manufacturing systems (HMS). This paper will address issues associated with HMS, and propose a reference architecture based on a design-to-control concept. The primary focus will be given to the collaborative and integrated design-to-control approach based on machining feature, agent technology, and function block standards. Emphasis is also extended and given to metamorphic process planning and control of HMS using multi-agent negotiation and cooperation. The proposed approach, together with the open architecture, shows much promise for improving the entire manufacturing system performance under the ever-changing real-time and distributed shop floor environments.

  • 257.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Machine availability monitoring and machining process planning towards Cloud manufacturing2013In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 6, no 4, p. 263-273Article in journal (Refereed)
    Abstract [en]

    Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing.

  • 258.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Overview of an Adaptive Setup Planning Approach for Job Shop Operations2009In: ASME 2009 International Manufacturing Science and Engineering Conference, ASME Press, 2009, Vol. 1, p. 221-229Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of an adaptive setup planning system that considers both the availability and capability of machines on a shop floor. It integrates scheduling functions at setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific optimal setup plans. The objective is to enable adaptive setup planning for dynamic machining job shop operations. Particularly, this paper documents basic algorithms and architecture of the setup planning system for dynamically assigned machines. It is then validated through a case study.

  • 259.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Planning towards Enhanced Adaptability in Digital Manufacturing2011In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 24, no 5, p. 378-390Article in journal (Refereed)
    Abstract [en]

    This paper presents an integrated approach for developing a web-based system with enhanced adaptability, including distributed process planning, real-time monitoring and remote machining. The objective is to develop a new methodology and relevant processing algorithms for enhancing adaptability in digital manufacturing. This approach is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilising the latest Java technologies (Java 3D and Java Servlet) for system implementation, it allows end-users to plan and control distant manufacturing operations based on runtime information from shop floors. Details on the principle of the Wise-ShopFloor framework, system architecture, and a prototype system are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based digital manufacturing.

  • 260.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Preface2014In: Dynamic Thermal Analysis of Machines in Running State, London, UK: Springer-Verlag , 2014, p. v-viiChapter in book (Refereed)
  • 261.
    Wang, Lihui
    National Research Council of Canada.
    Production Planning for Distributed Manufacturing2006In: International journal of computer applications in technology, ISSN 0952-8091, E-ISSN 1741-5047, Vol. 26, no 1-2, p. 1-2Article in journal (Refereed)
  • 262.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Strengthened Manufacturing Research at NAMRC 452017In: Manufacturing engineering, ISSN 0361-0853, Vol. 158, no 6, p. 17-17Article in journal (Refereed)
  • 263.
    Wang, Lihui
    National Research Council of Canada.
    Study on Methodology of Dynamic FEM Analysis for Integrated CAD/CAE System1998In: Proceedings of the 6th IASTED International Conference on Robotics and Manufacturing, 1998, p. 177-180Conference paper (Refereed)
  • 264.
    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)
  • 265.
    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)
  • 266.
    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.

  • 267.
    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)
  • 268. 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)
  • 269.
    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.

  • 270.
    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

  • 271.
    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.

  • 272. 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)
  • 273.
    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.

  • 274.
    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)
  • 275.
    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)
  • 276.
    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)
  • 277. 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)
  • 278.
    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.

  • 279.
    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.

  • 280.
    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)
  • 281.
    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)
  • 282.
    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.

  • 283.
    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.

  • 284.
    Wang, Lihui
    et al.
    National Research Council of Canada .
    Cai, Ningxu
    Feng, Hsi-Yung
    Toward Adaptive Job Shop Planning in Dynamic Environment2005Conference paper (Refereed)
  • 285.
    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.

  • 286.
    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)
  • 287.
    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)
  • 288.
    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.

  • 289.
    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.

  • 290.
    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)
  • 291.
    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.

  • 292.
    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. 

  • 293.
    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.

  • 294.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
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