Journal of the Korean Society of Manufacturing Technology Engineers (한국생산제조학회지)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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Injection Mold Cooling Circuit Optimization by Back-Propagation Algorithm

오류역전파 알고리즘을 이용한 사출성형 금형 냉각회로 최적화

  • Published : 2009.08.15
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Abstract

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

Keywords

References

  1. Jang, H. G., 2008, A study on automatic optimization of cooling circuit design in injection mold, M.S. Thesis, Ajou University, Republic of Korea
  2. Chung, H. W., 2008, A study of automated optimization of injection-mold cooling-line design with predefined product temperature, M.S. Thesis, Ajou university, Republic of Korea
  3. Lin, J. C., 2002, "Optimum cooling system design of free-form injection mold using an abductive network," Journal of Materials Processing Technology, Vol. 120, pp. 226-236.
  4. Kim, D. S., 1993, Neural networks theory and applications, Jinhan M&B, Republic of Korea
  5. Lippman, R. P., 1987, "An introducing to computing with neural nets," IEEE ASSP Magazine, pp. 4-22.
  6. Montgomery, D. C., 2005, Design and Analysis of Experiments, John Wiley & Sons, Hoboken, NJ, U.S.A.
  7. Choi, J. H., 2009, "A study on the reduction of design variables for injection mold cooling system optimization," Proceedings of 2009 Spring Conference of The Korean Society of Technology of Plasticity, pp. 361-364.