Development of Computer Aided Process Planning System for Rotational Components Having Form Features

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 259)

Abstract

This paper presents a feature based Computer Aided Process Planning (CAPP) system for rotational components. While developing a feature based CAPP system, a set of flexible process plans were encountered and a population based heuristics namely Genetic Algorithm (GA) is used to obtain a most optimal process plan for a defined objective function. The objective function for optimization of process plan is the minimization of manufacturing score. Proposed methodology has been implemented for an example part having different rotational features.

Keywords

Rotational parts CAPP Genetic algorithm 

References

  1. 1.
    Ssemakula, M.E.: Role of Process Planning in Integration of CAD/CAM Systems. SRC-TR, pp. 87–108 (1986)Google Scholar
  2. 2.
    Alting, L., Zhang, H.: Computer-aided process planning: the state-of-the-art survey. Int. J. Prod. Res. 27(4), 553–585 (1989)Google Scholar
  3. 3.
    Steudel, H.J.: Computer-aided process planning: past, present and future. Int. J. Prod. Res. 22(2), 253–266 (1984)Google Scholar
  4. 4.
    Weill, R., Spur, G., Eversheim, W.: Survey of computer aided process planning systems. Ann. CIRP 31(2), 539–551 (1982)Google Scholar
  5. 5.
    Cay, F., Chassapis, C.: An IT view on perspectives of computer aided process planning. Comput. Ind. 34, 307–337 (1997)Google Scholar
  6. 6.
    Zhang, F., Zhang, Y.F., Nee, A.Y.C.: Using genetic algorithms in process planning for job shop machining. IEEE Trans. Evol. Comput. 1(4), 278–289 (1997)Google Scholar
  7. 7.
    Davis, L.: Job shop scheduling with genetic algorithms. In: Greferstette, J.J. (ed.) Proceedings of 1st International Conference on Genetic Algorithms and Their Applications, pp. 136–140. Lawrence Erlbaum, Hillsdale, NJ (1985)Google Scholar
  8. 8.
    Biegel, J.E., Daver, J.: Genetic algorithms and job scheduling. Comput. Ind. Eng. 19, 81–91 (1990)Google Scholar
  9. 9.
    Suh, J.Y., Van Gucht, D.: Incorporating heuristic information into genetic search. In: Greferstette, J.J. (ed.) Proceedings of 2nd International Conference on Genetic Algorithms, pp. 100–107. Lawrence Erlbaum, Hillsdale, NJ (1987)Google Scholar
  10. 10.
    De Jong, K.A., Spears, W.M.: Using genetic algorithms to solve NP complete problems. In: Schaffer, J.D. (ed.) Proceedings of Third International Conference on Genetic Algorithms, pp. 124–132. Morgan Kaufmann, San Mateo, CA (1989)Google Scholar
  11. 11.
    Vancza, J., Markus, A.: Genetic algorithms in process planning. Comput. Ind. Eng. 17, 181–194 (1991)Google Scholar
  12. 12.
    Awadh, B., Sepehri, N., Hawaleshka, O.: A computer-aided process planning model based on genetic algorithms. Comput. Oper. Res. 22(8), 841–856 (1995)Google Scholar
  13. 13.
    Yip-Hoi, D., Dutta, D.: A genetic algorithm application for sequencing operations in process planning for parallel machining. IIE Trans. 28, 55–68 (1996)Google Scholar
  14. 14.
    Shunmugam, M.S., Mahesh, P., Bhaskara Reddy, S.V.: A method of preliminary planning for rotational components with C-axis features using genetic algorithm. Comput. Ind. 48, 199–217 (2002)Google Scholar
  15. 15.
    Kalpakjian, S., Schimid, S.R.: Manufacturing Engineering and Technology. Addison-Wisely Longman, Singapore (2000)Google Scholar
  16. 16.
    Dagli, C., Sittisathanchai, S.: Genetic neuro-scheduler for job shop scheduling. Comput. Ind. Eng. 25(1/4), 267–270 (1993)Google Scholar
  17. 17.
    Gopala Krishna, A.: Selection of optimal sequence of machining operation sequence in CAPP. J. Manuf. Eng. 2, 4 (2007)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringAditya Institute of Technology and ManagementTekkaliIndia
  2. 2.Department of Mechanical EngineeringNational Institute of TechnologyWarangalIndia

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