Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-25T21:20:28.610Z Has data issue: false hasContentIssue false
  • English
  • Français

A systematic approach for design and planning of mechanical assemblies

Published online by Cambridge University Press:  27 February 2009

C. L. Philip Chen  and
C. A. Wichman
C. L. Philip Chen
Affiliation:
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, U.S.A.
C. A. Wichman
Affiliation:
Directorate of Manufacturing and Quality Assurance, Wright-Patterson Air Force Base, OH 45433, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

A system that integrates design and planning for mechanical assemblies is presented. The system integrates neural network computing that captures designer's design concept and rule-based system to generate a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing qualitative assembly information. A neural network model together with feature-based model translates the input pattern into a preliminary boundary representation (B-rep). Based on a refinement B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. A robust part collision detection algorithm to generate the precedence relationships among the assembly's components is included in the system. By contrast with many assembly planning systems that used a prolonged question-and-answering session or required knowledge beyond what is typically available in the design database, an automated assembly planning system presented here draws input relationships directly from the conceptual design and the geometry of the assembly. The system developed under this study extracts all reasoning information from the product model and permits the components to be assembled in a multitude of directions. Several experiments illustrate the effectiveness of the designed assembly planning system.

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 1 , February 1993 , pp. 19 - 36
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abe, N., Yamada, S. and Tauji, S. 1986. A consulting system which detects and undoes erroneous operations by novices. Annals of the CIRP, 35, 435–442.Google Scholar
Boothroyd, G. and Dewherst, P. 1989. Product Design for Assembly, 2nd Edn, Rhode Island: Wakefile.Google Scholar
Bourjault, A. 1984. Contribution a une approach methologique de l'assemblage automatise: elaboration automatique des sequences operatoires. Thesis. L'Université de Franche-Comté.Google Scholar
Brownston, L. 1985. Programming Expert Systems in OPS5: An Introduction to Rule-Based Programming. Reading, MA: Addison-Wesley.Google Scholar
Carpenter, G. A. and Grossberg, S. 1987. ART2: Self-organization of Stable Category Recognition Codes for Analog Input Patterns. Applied Optics, 26, 4919–4930.Google Scholar
Chang, K. H. and Wee, W. G. 1988. A knowledge-based planning system for mechanical assembly using robots. IEEE Expert, 3(1), 18–30.CrossRefGoogle Scholar
Chen, C. L. P. 1991. Automatic assembly sequences generation by pattern-matching. IEEE Transactions on Systems, Man, and Cybernetics, 21(2), 376–389.CrossRefGoogle Scholar
Chen, C. L. P. and Wichman, C. 1992. A CLIPS rule-based assembly planning system. Proceedings of NSF 1992 Design and Manufacturing Grantees Conference, pp. 837–841.Google Scholar
De Fazio, T. L. and Whitney, D. E. 1987. Simplified generation of all mechanical assembly sequences. IEEE Journal of Robotics and Automation, 3(6), 640–658.CrossRefGoogle Scholar
Delchambre, A. 1990. A pragmatic approach to computer-aided assembly planning. Proceedings of IEEE International Conference on Robotics and Automation, pp. 1600–1605.Google Scholar
Delchambre, A. 1991. An automatic, systematic and user-friendly computer-aided planner for robotized assembly. Proceedings of IEEE International Conference on Robotics and Automation, pp. 592–598.Google Scholar
Giarrantano, J. and Riley, G. 1989. Expert systems: principles and programming. Boston, MA: PWS-Kent.Google Scholar
Homem de Mello, L. S. and Sanderson, A. C. 1986. An AND/OR graph representation of assembly plans. Proceedings of the 5th AAAI, 1113–1119.Google Scholar
Homem de Mello, L. S. and Sanderson, A. C. 1989. A correct and complete algorithm for the generation of mechanical assembly sequences. Proceedings of IEEE International Conference on Robotics and Automation, pp. 56–61.Google Scholar
Huang, Y. F. and Lee, C. S. G. 1989. Precedence knowledge in feature mating operation assembly planning. Proceedings of IEEE International Conference on Robotics and Automation, pp. 216–221.Google Scholar
Huang, Y. F. and Lee, C. S. G. 1990. An automated assembly planning system. Proceedings of IEEE International Conference on Robotics and Automation, pp. 1594–1599.Google Scholar
Huang, Y. F. and Lee, C. S. G. 1991. A framework of knowledge-based assembly planning. Proceedings of IEEE International Conference on Robotics and Automation, pp. 599–604.Google Scholar
Kroll, E., Lenz, E. and Wolberg, J. R. 1989. Rule-based generation of exploded views and assembly sequences. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3(3), 143–155.CrossRefGoogle Scholar
Lai, K. and Wilson, W. R. D. 1987. FDL: a language for function description and rationalization in mechanical design. Proceedings of Computers in Engineering Conference, pp. 87–94.Google Scholar
Lee, K. and Gossard, D. C. 1985. A hierarchical data structure for representing assemblies: Part 1. Computer-Aided Design, 17(1), 15–19.CrossRefGoogle Scholar
Lin, A. C. and Chang, T. C. 1990. Automated assembly planning. Proceedings of the NSF Grantees Conference, pp. 523–531.Google Scholar
Mantyla, M. 1988. An Introduction to Solid Modeling. Rockville, MD: Computer Science Press.Google Scholar
Mortenson, M. E. 1985. Geometric Modeling, New York: John Wiley.Google Scholar
Nnaji, B. O., Chu, J.-Y. and Akrep, M. 1988. A schema for CAD-based robot assembly task planning for CSG-modeled objects. Journal of Manufacturing Systems, 7(2), 131–145.CrossRefGoogle Scholar
Pao, Y-H. 1989. Adaptive Pattern Recognition and Neural Networks. New York: Addison Wesley.Google Scholar
Redford, A. 1984. Product design for general purpose assembly. Assembly Automation, 4(3), 133–136.CrossRefGoogle Scholar
Rosario, L. M. 1992. A computer-aided design approach to designing for ease of assembly. Proceedings of 1992 NSF Grantees Design and Engineering Conference, pp. 771–776.Google Scholar
Schubert, L. K. 1979. Problems with parts. Proceedings of 6th IJCAI, pp. 778–784.Google Scholar
Wolter, (1989). On the Automatic Generation of Assembly Plans. Proceedings of IEEE International Conference on Robotics and Automation, pp. 62–68.Google Scholar