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  • 51.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Collaborations towards adaptive manufacturing2012In: Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD) / [ed] Liang Gao, Weiming Shen, Jean-Paul Barthès, Junzhou Luo, Jianming Yong, Wenfeng Li & Weidong Li, IEEE conference proceedings, 2012, p. 14-21Conference paper (Refereed)
    Abstract [en]

    This paper presents a new approach for real-time collaborations in adaptive manufacturing, including web-based remote monitoring and control of an industrial robot, and active collision avoidance for human-robot collaborations. It is enabled by using virtual 3D models driven by real sensor data and depth images of human operators. The objectives of this research are to significantly reduce network traffic needed for real-time monitoring over the Internet and to increase the human safety in a human-robot coexisting environment. The results of a case study show that the approach consumes less than 1% of network bandwidth of traditional camera-based methods, and is feasible and practical as a web-based solution.

  • 52.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Combining Facility Layout Redesign and Dynamic Routing for Job-shop Assembly Operations2011In: Proceedings of 2011 IEEE International Symposium on Assembly and Manufacturing, IEEE, 2011, article id 5942302Conference paper (Refereed)
    Abstract [en]

    This paper presents a hybrid approach for facility layout redesign and dynamic job routing. More specifically, based on the source of uncertainty, the facility layout problem is split into two sub-problems and dealt with by two modules: relayout and find-route. Genetic algorithm is used where changes may cause a layout redesign of the entire shop, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. The method is verified in a case study of a hypothetic robotic assembly shop.

  • 53.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Disassembly Planning by Motion Tracking Analysis2011In: Proceedings of International Conference on Remanufacturing, University of Strathclyde , 2011, p. 182-186Conference paper (Refereed)
    Abstract [en]

    This paper presents an integrated intuitive system for disassembly planning by actively tracking the motion of an experienced operator. It can also be used for operators training by combining a virtual reality (VR) environment with the motion tracking. The developed conceptual prototype for disassembly planning and training enables individuals to interact with a virtual environment in real time. It extends the technology of motion tracking and integrates it with virtual environment technology to create real-time virtual work cell simulations in which disassembly operators may be immersed with hands-on experiences. In addition to the operators training, the experimental results to date are presented to demonstrate the potential contributions of human skills in achieving effective disassembly planning for remanufacturing. It is expected that this approach will lead to environment-friendly and sustainable operations by conserving energy and cost that are first tested in a human-emerged virtual system.

  • 54.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 55.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 56.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2012Collection (editor) (Refereed)
  • 57.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 58.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 59.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
  • 60.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
  • 61.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2010Collection (editor) (Refereed)
  • 62.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2009Collection (editor) (Refereed)
  • 63.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2009Collection (editor) (Refereed)
  • 64.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    International Journal of Manufacturing Research2011Collection (editor) (Refereed)
  • 65.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    International Journal of Manufacturing Research2009Collection (editor) (Other academic)
  • 66.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Overview of an adaptive setup planning approach for job shop operations2009In: ASME 2009 International Manufacturing Science and Engineering Conference, Volume 1: Advances in Manufacturing Process Planning and Scheduling, ASME Press, 2009, 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.

  • 67.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wise-Shop Floor: Linking Virtual Manufacturing to Real Productions2009In: The International 3rd Swedish Production Symposium / [ed] B. G. Rosén, The Swedish Production Academy , 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.

  • 68.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    De Montfort University, UK.
    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: June 26 - 29th 2011, Feng Chia University, Taiwan / [ed] F. Frank Chen ..., Society of Lean Enterprise Systems of Taiwan , 2011, 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.

  • 69.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Feng, Hsi-YungThe University of British Columbia.
    Journal of Manufacturing Systems2011Collection (editor) (Refereed)
  • 70.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Web-DPP: An adaptive approach to planning and monitoring of job-shop machining operations2011In: Proceedings of the 7th CIRP-Sponsored International Conference on Digital Enterprise Technology, Athens, Greece, 2011, University of Patras , 2011Conference 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.

  • 71.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Givehchi, Mohammad
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Robotic Assembly Planning and Control with Enhanced Adaptability2012In: 45th CIRP Conference on Manufacturing Systems 2012 / [ed] G. Chryssolouris, D. Mourtzis, Elsevier, 2012, Vol. 3, p. 173-178Conference paper (Refereed)
    Abstract [en]

    The dynamic market today requires manufacturing companies to possess high degree of adaptability and flexibility in order to deal with shop-floor uncertainties. Such uncertainties as missing tools, part shortage, job delay, rush-order and unavailability of resources, etc. happen more often in assembly operations. Targeting this problem, this research proposes a function block enabled approach to achieving adaptability and flexibility in assembly planning and control. In particular, this paper presents our latest development using a robotic mini assembly cell for testing and validation of a function block enabled system capable of assembly and robot trajectory planning and control. It is expected that a better adaptability can be achieved by this approach.

  • 72.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Koh, S. C. LennyManagement School, Logistics and Supply Chain Management (LSCM) Research Centre, Sheffield University, UK.
    Enterprise Networks and Logistics for Agile Manufacturing2010Collection (editor) (Other academic)
  • 73.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ma, Ji
    The University of Western Ontario, Canada.
    Feng, Hsi-Yung
    The University of British Columbia, Canada.
    Web-DPP: A Distributed Process Planning System for Adaptive Machining Operations2009In: Proceedings of the 19th International Conference on Flexible Automation and Intelligent Manufacturing: FAIM 2009 / [ed] Farhad Nabhani, Catherine Frost, Sara Zarei, Munir Ahmad, William. G. Sullivan, Gemini International Ltd , 2009, p. 186-193Conference paper (Refereed)
    Abstract [en]

    Outsourcing, joint ventures, and cross-border collaborations have led to work environments geographically distributed across organizational and national boundaries. Targeting the distributed environment, the Web has been widely used for development of collaborative applications supporting dispersed working groups and organizations, because of its platform, network and operating system transparency, and its easy-to-use browser interface. The objective of this research is to develop an innovative Web-based distributed process planning system (Web-DPP) for machining operations. 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 presents both the system design specification and the latest development of this system.

  • 74.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Ma, Ji
    Department of Mechanical and Materials Engineering, University of Western Ontario, London, Canada.
    Feng, Hsi-Yung
    Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada.
    Web-DPP: towards job-shop machining process planning and monitoring2011In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 6, no 4, p. 337-353Article in journal (Refereed)
    Abstract [en]

    Outsourcing, joint ventures and cross-border collaborations have led to job environments geographically distributed. Targeting the distributed environment, the web has been widely used for developing collaborative applications due to its platform, network and operating system transparency and easy-to-use interface. Utilising the existing infrastructure, the objective of this research is to develop an integrated Web-based Distributed Process Planning (Web-DPP) system for job-shop machining operations and execution monitoring. Our approach engages a dispersed working group in a collaborative environment, allowing team members to share real-time information through the Web-DPP. This paper presents both the system design specification and the latest development.

  • 75.
    Wang, Lihui
    et al.
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Mohammed, Abdullah
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Xi Vincent
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Energy-efficient robot applications towards sustainable manufacturing2018In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 8, p. 692-700Article in journal (Refereed)
    Abstract [en]

    The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a robot path and based on the inverse kinematics and dynamics of the robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the path. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly. This cloud-based energy-efficient approach for robotic applications can largely enhance the current practice as demonstrated by the results of three case studies, leading towards sustainable manufacturing.

  • 76.
    Wang, Lihui
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Mohammed, Abdullah
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Xi Vincent
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Recent Advancements of Smart Manufacturing: An Example of Energy-Efficient Robot2016In: Proceedings of the 26th International Conference on Flexible Automation and Intelligent Manufacturing, 2016, p. 884-892Conference paper (Refereed)
    Abstract [en]

    The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a trajectory and based on the inverse kinematics and dynamics of a robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the trajectory. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly.

  • 77.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Song, Yijun
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Gao, Qiaoying
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Designing function blocks for distributed process planning and adaptive control2009In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 22, no 7, p. 1127-1138Article in journal (Refereed)
    Abstract [en]

    The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.

  • 78.
    Wang, Lihui
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, Sweden.
    Wang, Wei
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, Sweden.
    Liu, Dawei
    AVIC Chengdu Aircraft Industrial (Group) Ltd. Co., China.
    Dynamic feature based adaptive process planning for energy-efficient NC machining2017In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 66, no 1, p. 441-444Article in journal (Refereed)
    Abstract [en]

    This paper presents a dynamic feature based adaptive process planning approach that can optimise machining cost, machining time and energy consumption simultaneously. The material removal volume of a dynamic feature is refined into non-overlapping volumes removed respectively by a single machining operation in which unified cutting mode is performed. Benefitting from this refinement, energy consumption is estimated analytically based on instantaneous cutting force as a function of real cutting parameters. Moreover, the cutting parameters assigned to each machining operation are optimised effectively in the unified cutting mode. This novel approach enhances the energy efficiency of NC machining through process planning.

  • 79.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Yang, Xiaoyu
    School of Applied Science & Technology, Fanshawe College, London, ON, Canada.
    Assembly operator training and process planning via virtual systems2011In: International Journal of Sustainable Engineering, ISSN 1939-7038, E-ISSN 1939-7046, Vol. 4, no 1, p. 57-67Article in journal (Refereed)
    Abstract [en]

    In this paper, we present an integrated intuitive system for assembly operators training and assembly process planning by combining virtual reality with motion-tracking technologies. The developed conceptual prototype for assembly planning and training enables individuals to interact with a virtual environment in real time. It extends the new technologies of motion tracking and integrates them with virtual environment technologies to create real-time virtual work cell simulations in which assembly operators may be immersed with hands-on experiences. In addition to operators training, the experimental results to date are presented to demonstrate the potential contributions of human skills in achieving effective assembly planning including disassembly operations. It is expected that this approach will lead to environment-friendly and sustainable operations by conserving energy and cost that are first tested in a human-emerged virtual system.

  • 80.
    Yang, Xiaoyu
    et al.
    National Research Council of Canada.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    VR-Assisted Assembly Planning2009In: The International 3rd Swedish Production Symposium / [ed] B. G. Rosén, The Swedish Production Academy , 2009, p. 423-428Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an intelligent assembly planning and training system combining virtual reality (VR) and motion tracking technologies. The development conceptual prototype for assembly planning and training enables individuals to interact with virtual environment in real time. It extends the new technologies of motion tracking and integrates them with virtual environment technologies to create real-time virtual work cell simulations in which workers may be immersed with hands-on experiences. The experimental results to date are presented to demonstrate the contributions of human skills in achieving intelligent assembly planning.

12 51 - 80 of 80
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