IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

Estimation of Process Parameters Using QFD and Neural Networks in Injection Molding

품질기능전개와 신경망 회로를 이용한 사출성형 공정변수의 예측

  • Koh, Bum-Wok (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Kim, Jong-Seong (Department of Systems Management Engineering, Sungkyunkwan University) ;
  • Choi, Hoo-Gon (Department of Systems Management Engineering, Sungkyunkwan University)
  • 고범욱 (성균관대학교 산업공학과) ;
  • 김종성 (성균관대학교 산업공학과) ;
  • 최후곤 (성균관대학교 산업공학과)
  • Received : 20071000
  • Accepted : 20080300
  • Published : 2008.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The injection molding process is able to produce high precision manufactures as a single process with fast speed. However, the prices of both the mold and the molding machine are expensive, and the single process is very complex and difficult to compose of the exact relationship between the process setting conditions and the product quality. Therefore, the quality of a molded product often depends on a skillful engineer's operations in the design of both parts and molds. In this paper, the relationship between the process conditions and the defectiveness is built for better manufactures under settings of the appropriate parameters, and so it can reduce the setup time in the injection molding process. Quality Function Deployment (QFD) provides severe defectiveness factors along with the related process parameters. Also, neural networks estimate the relationship between defective factors and process setting parameters, and lead to reduce the defectiveness of molded parts.

Keywords

References

  1. Bralla, J. G. (1998), Design for Manufacturability Handbook, 2nd Edition, Chap. 6, McGraw-Hill
  2. Choi, W. J. (2000), A Study on the Optimization of Injection Molding Processing Conditions using Genetic Algorithm, MS thesis, Seoul National University, Korea
  3. Jeong, Y. D. and Goo, B. H. (2005), Product and Mold Design by Injection Molding Analysis, Chap. 3, Intervision, Korea
  4. Kim, J. W., Kim, C. S., Kim, H. D. (2001), The Newest Injection Molding Design, Chap. 11, Sunhak, Korea
  5. Kwak, S. W.(1999), A Study on the Optimization of Process Parameters in Molding, Ph.D dissertation, Seoul National University, Korea
  6. Lau, H. C. W., Wong, T. T., and Pun K. F. (1999), Neural-fuzzy modeling of injection molding machine for intelligent control, Expert Systems with Applications, 17, 33-43
  7. Lee, S. C. (1997), Shrinkage Analysis of Injection Molded Parts Using Numerical Simulation and Neural Network, Ph.D dissertation, Korea Advanced Institute of Science and Technology, Korea
  8. Lee, S. C. (2001), Plastic Injection Molding-Condition Control Method, Chap. 3, Kijeon Research, Korea
  9. Lim, S. H. (2001), Injection Molding Design, Chap. 14, Bosunggak, Korea
  10. Nguyen, L. T., Danker, A., Santhiran, N., and Shervin, C. R. (1992), Flow Modeling of Wire Sweep during Molding of Intergrated Circuits, PED, 60, 39-46
  11. Noh, W. G., Jeong, J. S., Hwang, G. S. (2006), Molding Injection and Product Design toward World Best, Chap. 3, Kyobo Books, Korea
  12. Ozcelik, B. and Erzurumlu, T. (2006), Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm, Journal of Materials Processing Technology, 171(3), 437-445
  13. Sadeghi, B. H. M. (2000), A BP-neural network predictor model for plastic injection molding process, Journal of Materials Processing Technology, 103, 411-416 https://doi.org/10.1016/S0924-0136(00)00498-2
  14. Shelesh-Nezhad, K. and Siores, E. (1997), An Intelligent System for Plastic Injection Molding Design, Journal of Materials Processing Technology, 63, 458-462 https://doi.org/10.1016/S0924-0136(96)02664-7
  15. Son, Y. E. (1998), Injecton Molding Technology, Chap. 11, Kuminsa, Korea
  16. Spina, R. (2004), Injection moulding of automotive components: comparison between hot runner systems for a case study, Journal of Materials Processing Technology, 155/156, 1497-1504
  17. Yoo, Y. S. (2002), The Newest Injection Molding, Chap. 7, Kijeon Research, Korea
  18. http://www.3dcontentcentral.com