DOI QR코드

DOI QR Code

Optimal Control System of Traverse Grinding

트래버스 연삭의 최적 제어시스템

  • Choi, Jeongju (Technical Center for High Performance Valves, Dong-a University)
  • 최정주 (동아대학교 고기능성밸브기술지원센터)
  • Received : 2012.09.17
  • Accepted : 2012.12.06
  • Published : 2012.12.31

Abstract

In this paper, the algorithm to determine the optimal condition of traverse grinding is proposed by using differential evolution algorithm(DEA). The cost function to determine the optimal grinding condition is designed with considering process cost, production rate, surface roughness. Also, the constraint conditions for grinding such as thermal damage effect, machine tool stiffness, wear parameter of grinding wheel, surface roughness are considered. The algorithm is implemented with LabView software which is widely used at the industrial field. The performance of proposed algorithm is verified by comparing with the result of genetic algorithm(GA) through computer simulation.

본 논문에서는 DEA(Differential Evolution Algorithm)기법을 이용하여 트래버스 연삭의 최적 조건을 선정하기 위한 알고리즘을 제안하였다. 최적 연삭 조건 선정을 위한 가격함수는 가공 경비, 생산율 및 표면 거칠기 등의 다중 함수식을 이용하였다. 또한 연삭 조건에 대한 구속 조건으로 열 손상 효과, 가공 툴의 강성, 연삭 휠 마모 상수 및 표면 거칠기 등을 고려하였다. 알고리즘의 구현은 산업현장에서 널리 사용되는 LabView소프트웨어를 사용하였다. 제안된 알고리즘의 성능은 컴퓨터 시뮬레이션을 통해 GA알고리즘의 결과와 비교하여 검증하였다.

Keywords

References

  1. B. V. Babu, and M. M. L. Jehan, "Differential Evolution for Multi-objective Optimization," In Proceedings of the IEEE Congress on Evolutionary Computation, Canberra, Australia, Vol. 4, pp. 2696-2703, 2003.
  2. T. J. Choi, N. Subrahmanya, and Y. C. Shin, "Generalized practical models of cylincrical plunge grinding proceses," Machine Tools and Manufacture, Vol. 48, pp. 61-72, 2008, Article(CrossRefLink) https://doi.org/10.1016/j.ijmachtools.2007.07.010
  3. S. Xiao, and S, Markin, "On-line Optimization for Internal Plunge Grinding," CIRP- Manufacturing Technology, Vol. 45, No. 1, pp. 287-292, 1996, Article(CrossRefLink) https://doi.org/10.1016/S0007-8506(07)63065-0
  4. R. Saravanan, P. Asokan, and M. Sachidanandam, "Genetic Algorithm Approach for Optimization of Surface Grinding Operations," International Jouranl fo Machine Tool and Manufacture, Vol. 42, pp. 1327-1334, 2002. https://doi.org/10.1016/S0890-6955(02)00074-3
  5. J. Choi, and J. H. Park,"Development of Roughness Estimation Model for Plunge Grinding of Valve Parts Using Neural Network," Journal of the Korea Academia-Industrial cooperation Society, Vol. 12, No. 1, pp. 62-67, 2011. https://doi.org/10.5762/KAIS.2011.12.1.062
  6. J. Choi, C. W. Lee. and J. H. Park, "Developement of the Process Model for Plunge Grinding and Optimization of Grinding Process," Journal of Mechanical Engineering Science, Vol. 225, pp. 2628-2637, 2011, Article(CrossRefLink) https://doi.org/10.1177/0954406211406201
  7. A. G. Krishna, "Optimization of surface grinding operations using a differential evolution approach," Journal of Materials Processing Technology, Vol. 183, pp. 202-209, 2007, Article(CrossRefLink) https://doi.org/10.1016/j.jmatprotec.2006.10.010
  8. N. Baskar, R. Saravanan, P. Asokan, and G. Prabhaharan, "Ants Colony Algorithm Approach for Multi-objective Optimization of Surface Grinding Operations," International Journal of Advanced Manufacturing Technology, Vol. 23, pp. 311-317, 2004, Article(CrossRefLink) https://doi.org/10.1007/s00170-002-1533-6
  9. N. Baskar, R. Saravanan, P. Asokan, and G. Prabhaharan, "Ants Colony Algorithm Approach for Multi-objective Optimization of surface grinding operations," International Journal of Advanced Manufacturing Technology, Vol. 23, pp. 311-317, 2004, Article(CrossRefLink) https://doi.org/10.1007/s00170-002-1533-6
  10. X. M. Wen, A. A. O. Tay, and A. Y. C. Nee, "Micro-computer-based Optimization fo the Surface Grinding Process," Journal of Materials Processing Technology, Vol. 29, pp. 75-90, 1992, Article(CrossRefLink) https://doi.org/10.1016/0924-0136(92)90426-S