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Journal of Intelligent Manufacturing

, Volume 17, Issue 5, pp 557–569 | Cite as

A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts

  • Sankha Deb
  • Kalyan Ghosh
  • S. Paul
Article

Abstract

The relevant literature on machining operations selection in Computer-Aided Process Planning (CAPP) by decision trees, expert systems and neural networks has been reviewed, highlighting their contributions and shortcomings. This paper aims at contributing to the applicability of back-propagation neural network method for the selection of all possible operations for machining rotationally symmetrical components, by prestructuring the neural network with prior domain knowledge in the form of heuristic or thumb rules. It has been achieved by developing two forms of representation for the input data to the neural network. The external representation is used to enter the crisp values of the input decision variables (namely the feature type and its attributes such as diameter or width, tolerance and surface finish). The purpose of internal representation is to categorize the above crisp values into sets, which correspond to all the possible different ranges of the above input variables encountered in the antecedent ‘IF’ part of the thumb rules mentioned above. The input layer of the neural network has been designed in such a way that one neuronal node is allocated for each of the feature types and the sets of various feature attributes. In the output layer of the neural network, one neuronal node is allocated to each of the various feasible machining operation sequences found in the consequent ‘THEN’ part of the thumb rules. A systematic method for training of the neural network has been presented with the above thumb rules used to serve as guidelines for choosing the input patterns of the training examples. This method simplifies the process of training, reduces the time for preparation of training examples and hence the time to develop the overall process planning system. It can further help ensure that the entire problem domain is represented in a better manner and improve the quality of response of the neural network. The example of an industrially-relevant rotationally symmetrical workpiece has been analyzed using the proposed approach to demonstrate its potential for use in the real manufacturing environment. By this novel methodology, workpieces of complex shapes can be handled by investing a very limited amount of time, hence making it attractive and cost effective for industrial applications.

Keywords

Computer-aided process planning Machining process selection Alternative operation sequences Rotational parts Artificial neural networks 

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References

  1. Bralla, J.G. 1986Handbook of product design for manufacturing: A practical guide to low-cost productionMcGraw-HillNew York, USAGoogle Scholar
  2. Devireddy, C.R., Eid, T., Ghosh, K. 2002Computer-aided process planning for rotational components using artificial neural networksInternational Journal of Agile Manufacturing52749Google Scholar
  3. Devireddy, C.R., Ghosh, K. 1999Feature-based modeling and neural networks-based CAPP for integrated manufacturingInternational Journal Computer Integrated Manufacturing126174CrossRefGoogle Scholar
  4. Halevi, G., Weill, R.D. 1995Principles of process planning: a logical approachChapman and HallUKGoogle Scholar
  5. Jiang, B., Lau, H., Chang, F.T.S., Jiang, H. 1999An automatic process planning system for the quick generation of manufacturing process plans directly from CAD drawingsJournal of Materials Processing Technology8797106CrossRefGoogle Scholar
  6. Khoshnevis, B., Tan, W. 1995Automated process planning for hole-makingAmerican Society of Mechanical Engineers.Manufacturing Review8106113Google Scholar
  7. Knapp, Gerald M., & Wang, H. (1992). Neural networks in acquisition of manufacturing knowledge. In A. Kusiak (Ed.), Intelligent design and manufacturing, New York: Wiley Sons Inc.Google Scholar
  8. Neuframe Version 4. (2000). Getting started manual. Southampton, UK: Neusciences Intelligent Solutions.Google Scholar
  9. Radwan, A. 2000A practical approach to a process planning expert system for manufacturing processesJournal of Intelligent Manufacturing,117584CrossRefGoogle Scholar
  10. Sabourin, L., Villeneuve, F. 1996OMEGA, an expert CAPP systemAdvances in Engineering Software255159CrossRefGoogle Scholar
  11. Wang, K. 1998An integrated intelligent process planning system (IIPPS) for machiningJournal of Intelligent Manufacturing9503514CrossRefGoogle Scholar
  12. Wang, H., Li, J. 1991Computer-aided process planningElsevier Science Publishers B VAmsterdam, NetherlandsGoogle Scholar
  13. Wong, T.N., Siu, S.L. 1995A knowledge-based approach to automated machining process selection and sequencingInternational Journal of Production Research3334653484Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Mathematics and Industrial Engineering, Ecole PolytechniqueUniversity of MontrealMontrealCanada
  2. 2.Department of Mechanical EngineeringIndian Institute of TechnologyKharagpurIndia

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