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DPP: An agent-based approach for distributed process planning

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Abstract

A changing shop floor environment characterized by larger variety of products in smaller batch sizes requires creating an intelligent and dynamic process planning system that is responsive and adaptive to the rapid adjustment of production capacity and functionality. In response to the requirement, this research proposes a new methodology of distributed process planning (DPP). The primary focus of this paper is on the architecture of the new process planning approach, using multi-agent negotiation and cooperation. The secondary focus is on the other supporting technologies such as machining feature-based planning and function block-based control. Different from traditional methods, the proposed approach uses two-level decision-making—supervisory planning and operation planning. The former focuses on product data analysis, machine selection, and machining sequence planning, while the latter considers the detailed working steps of the machining operations inside of each process plan and is accomplished by intelligent NC controllers. By the nature of decentralization, the DPP shows promise of improving system performance within the continually changing shop floor environment.

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References

  • Arezoo, B., Ridgway, K. and Al-Ahmari, A. M. A. (2000) Selection of cutting tools and conditions of machining operations using an expert system. Computers in Industry, 42, 43-58.

    Google Scholar 

  • Boogert, R. M., Kals, H. J. and van Houten, F. J. (1996) Tool paths and cutting technology in computer-aided process planning. International Journal of Advanced Manufacturing Technology, 11, 186-197.

    Google Scholar 

  • Chang, P. T. and Chang, C. H. (2000) An integrated artificial intelligent computer-aided process planning system. International Journal of Computer Integrated Manufacturing, 13(6), 483-497.

    Google Scholar 

  • Devieddy, C. R. and Ghosh, K. (1999) Feature-based modeling and neural network-based CAPP for integrated manufacturing. International Journal of Computer Integrated Manufacturing, 12(1), 61-74.

    Google Scholar 

  • Dornfeld, D., Wright, P. K., Wang, F.-C., Sheng, P., Stori, J., Sundararajan, V., Krishnan, N. and Chu, C.-H. (1999) Multi-agent process planning for a networked machining service, in Technical Paper of North American Manufacturing Research Conference (NAMRC XXVII), MS99-175, 1-6.

    Google Scholar 

  • Edalew, K. O., Abdalla, H. S. and Nash, R. J. (2001) A computer-based intelligent system for automatic tool selection. Materials and Design, 22, 337-351.

    Google Scholar 

  • Giusti, F., Santochi, M. and Dini, G. (1986) COATS: an expert module for optimal tool selection. Annals of the CIRP, 35(1), 337-340.

    Google Scholar 

  • Gu, P., Balasubramanian, S. and Norrie, D. H. (1997) Bidding-based process planning and scheduling in a multi-agent system. Computers & Industrial Engineering, 32(2), 477-496.

    Google Scholar 

  • Hashmi, K., El Baradie, M. A. and Ryan, M. (1998) Fuzzy logic based intelligent selection of machining parameters. Computers & Industrial Engineering, 35(3;4), 571-574.

    Google Scholar 

  • IEC Technical Committee (1997) Function blocks for industrial-process measurement and control systems, Part 1: Architecture, IEC-TC65/WG6 Committee Draft.

  • Jennings, N. R. and Wooldridge, M. J. (1998) Applications of intelligent agents. Agent Technology: Foundations, Applications, and Markets, Jennings, N. R. and Wooldridge, M. J. (eds.), Springer, pp. 3-28.

  • Jung, J. Y. and Ahluwalia, R. S. (1993) Prismatic part feature extraction and feature-based tool path selection. Journal of Design and Manufacturing, 3, 1-19.

    Google Scholar 

  • Lee, K. Y. and Jung, M. Y. (1995) Flexible process sequencing using Petri net theory. Computers & Industrial Engineering, 28(2), 279-290.

    Google Scholar 

  • Lee, K. I., Lee, J. W. and Lee, J. M. (1989) Pattern recognition and process planning for prismatic workpieces by knowledge based approach. Annals of the CIRP, 38(1), 485-488.

    Google Scholar 

  • Lewis, R. (1997) Design of distributed control systems in the next millennium. Computing & Control Engineering Journal, 148-152.

  • Lewis, R. (2001) Modelling Control Systems Using IEC 61499: Applying Function Blocks to Distributed Systems, The Institution of Electrical Engineers, London, UK.

    Google Scholar 

  • Lim, T., Corney, J., Ritchie, J. M. and Clark, D. E. R. (2001) Optimizing tool selection. International Journal of Production Research, 39(6), 1239-1256.

    Google Scholar 

  • Lin, A. C. and Wei, C.-L. (1997) Automated selection of cutting tools based on solid models. Journal of Materials Processing Technology, 72, 317-329.

    Google Scholar 

  • Mehrabi, M. G., Ulsoy, A. G. and Koren, Y. (2000) Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 11, 403-419.

    Google Scholar 

  • Monostori, L., Viharos, Zs. J. and Markos, S. (2000) Satisfying various requirements in different levels and stages of machining using one general ANN-based process model. Journal of Materials Processing Technology, 107, 228-235.

    Google Scholar 

  • Morad, N. and Zalzala, A. (1999) Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing, 6, 169-179.

    Google Scholar 

  • Nieble, B. W. (1965) Mechanized process selection for planning new designs. ASTME, paper 737.

  • Ong, S. K. and Nee, A. Y. C. (1996) Fuzzy-set-based approach for concurrent constraint set-up planning. Journal of Intelligent Manufacturing, 7(2), 107-120.

    Google Scholar 

  • Park, H. G. and Baik, J. M. (1999) Enhancing manufacturing product development through learning agent system over internet. Computers & Industrial Engineering, 37, 117-120.

    Google Scholar 

  • Rho, H. M., Geelink, R., Erve, A. H. V. and Kals, H. J. J. (1992) An integrated cutting tool selection and operation sequencing method. Annals of the CIRP, 41(1), 517-520.

    Google Scholar 

  • Shen W. and Norrie, D.H. (1999) Agent-based systems for intelligent manufacturing: A state-of-the-art survey. Knowledge and Information Systems, an International Journal, 1(2), 129-156.

    Google Scholar 

  • Shih, W. and Srihari, K. (1995) Distributed artificial intelligence in manufacturing systems control. Computers & Industrial Engineering, 29(1;4), 199-203.

    Google Scholar 

  • Shpitalni, M. and Fischer, A. (1991) CSG representation as a basis for extraction of machining features. Annals of the CIRP, 40(1), 157-160.

    Google Scholar 

  • Sluga, A., Butala, P. and Bervar, G. (1998) A multi-agent approach to process planning and fabrication in distributed manufacturing. Computers & Industrial Engineering, 35(3;4), 455-458.

    Google Scholar 

  • Smith, C. S. and Wright, P. K. (1996) CyberCut: a World Wide Web based design-to-fabrication tool. Journal of Manufacturing Systems, 15(6), 432-442.

    Google Scholar 

  • Sormaz, D. N. and Khoshnevis, B. (1997) Process planning knowledge representation using an object-oriented data model. International Journal of Computer Integrated Manufacturing, 10(1;4), 92-104.

    Google Scholar 

  • Stori, J. A. and Wright, P. K. (1996) A knowledge-based system for machining operation planning in feature based, open-architecture manufacturing, in Proceedings of ASME Design Technical Conference, Irvine, CA.

  • Sun, J., Zhang, Y. F. and Nee, A. Y. C. (2001) A distributed multi-agent environment for product design and manufacturing planning. International Journal of Production Research, 39(4), 625-645.

    Google Scholar 

  • Tseng, Y.-J. and Joshi, S. B. (1994) Recognizing multiple interpretations of interacting machining features. Computer-Aided Design, 26(9).

  • Tu, Y., Chu, X. and Yang, W. (2000) Computer-aided process planning in virtual one-of-a-kind production. Computers in Industry, 41, 99-110.

    Google Scholar 

  • Vosniakos, G. C. and Davies, B. J. (1993) Knowledge-based selection and sequencing of hole-making operations for prismatic parts. International Journal of Advanced Manufacturing Technology, 8, 9-16.

    Google Scholar 

  • Wang, L., Balasubramanian, S., Norrie, D. H. and Brennan, R. W. (1998) Agent-based control system for next generation manufacturing, in Proceedings of IEEE ISIC/CIRA/ISAS Joint Conference on Science and Technology of Intelligent Systems, pp. 78-83.

  • Wang, L. and Feng, H.-Y. (2002) An architecture for distributed process planning using function blocks. Transactions of the North American Manufacturing Research Institution of SME, 30, 613-620.

    Google Scholar 

  • Wang, L. and Norrie, D. H. (2001) Process planning and control in a holonic manufacturing environment. Journal of Applied Systems Studies, 2(1), 106-126.

    Google Scholar 

  • Wang, L., Zhao, W., Ma'ruf A. and Hoshi, T. (1996) Setupless fabrication technology incorporated with machining feature-based CAD/CAM system, in Proceedings of the International Manufacturing Engineering Conference, pp. 95-97.

  • Wu, R.-R. and Zhang, H.-M. (1998) Object-oriented and fuzzy-set-based approach for set-up planning. International Journal of Advanced Manufacturing Technology, 14, 406-411.

    Google Scholar 

  • Yeo, S. H., Ngoi, B. K. A. and Chen, H. (1998) Process sequence optimization based on a new cost-tolerance model. Journal of Intelligent Manufacturing, 9, 29-37.

    Google Scholar 

  • Zhang, Y., Feng, S. C., Wang, X., Tian, W. and Wu, R. (1999) Object oriented manufacturing resource modeling for adaptive process planning. International Journal of Production Research, 37(18), 4179-4195.

    Google Scholar 

  • Zhang, F., Zhang, Y. F. and Nee, A. Y. C. (1997) Using genetic algorithms in process planning for job shop machining. IEEE Transactions on Evolutionary Computation, 1(4), 278-289.

    Google Scholar 

  • Zhao, F. L., Tso, S. K. and Wu, P. S. Y. (2000) A cooperative agent modeling approach for process planning. Computers in Industry, 41, 83-97.

    Google Scholar 

  • Zhao, F. L. and Wu, P. S. Y. (1999) A cooperative framework for process planning. International Journal of Computer Integrated Manufacturing, 12(2), 168-178.

    Google Scholar 

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Authors and Affiliations

  1. National Research Council Canada, Integrated Manufacturing Technologies Institute, 800 Collip Circle, London, ON, N6G 4X8, Canada

    Lihui Wang & Weiming Shen

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  1. Lihui Wang
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  2. Weiming Shen
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Wang, L., Shen, W. DPP: An agent-based approach for distributed process planning. Journal of Intelligent Manufacturing 14, 429–439 (2003). https://doi.org/10.1023/A:1025797124367

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  • DOI: https://doi.org/10.1023/A:1025797124367

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