Rapid Data Acquisition System for Complex Algorithm Testing in Plastic Molding Industry
Commenced in January 2007
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Rapid Data Acquisition System for Complex Algorithm Testing in Plastic Molding Industry

Authors: A. Tellaeche, R. Arana

Abstract:

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in necessary algorithms for plastic injection process and monitoring, and also a flexible data acquisition system that allows rapid implementation of complex algorithms to assess their correct performance and can be integrated in the quality control process. This is the main topic of this paper. Finally, to demonstrate the performance achieved by this combination, a real case of use is presented.

Keywords: Plastic injection, machine learning, rapid complex algorithm prototyping.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087113

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References:


[1] Chung-Feng J. K., Te-Li S, “Optimization of multiple quality characteristics for polyether ether ketone injection molding process”, Fibers and Polymers, Dec 2006, Volume 7, Issue 4, pp 404-413.
[2] Chung-Feng J. K., Te-Li S., “Optimization of Injection Molding Processing Parameters for LCD Light-Guide Plates”, Journal of Materials Engineering and Performance, Oct 2007, Volume 16, Issue 5, pp 539-548.
[3] Oktem, Tuncay Erzurumlu, Ibrahim Uzman, “Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part”, Materials & Design, Volume 28, Issue 4, 2007, Pages 1271–1278.
[4] Jie-Ren S., “Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network”, The International Journal of Advanced Manufacturing Technology, April 2008, Volume 36, Issue 11-12, pp 1091-1103.
[5] Sadeghi B.H.M., “A BP-neural network predictor model for plastic injection molding process” Journal of Materials Processing Technology, Volume 103, Issue 3, 17 July 2000, Pages 411–416.
[6] Ozcelik B., Erzurumlu T.,” Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm”, Journal of Materials Processing Technology, Volume 171, Issue 3, 1 February 2006, Pages 437–445.
[7] Shi F., Lou Z.L., Zhang Y.Q., F. Shi, Lu J.G., “Optimisation of Plastic Injection Moulding Process with Soft Computing”, The International Journal of Advanced Manufacturing Technology, June 2003, Volume 21, Issue 9, pp 656-661.
[8] Chen W.C, Tai P.H., Wang M.W, Deng W.J., Chen C.T. “A neural network-based approach for dynamic quality prediction in a plastic injection molding process”. Expert Systems with Applications, Volume 35, Issue 3, October 2008, Pages 843–849.
[9] Zhu J., Chen J.C., “Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations” The International Journal of Advanced Manufacturing Technology, June 2006, Volume 29, Issue 3-4, pp 308-316.
[10] Shi F., Lou Z.L., Zhang Y.Q, Lu J.G, “Optimisation of Plastic Injection Moulding Process with Soft Computing” The International Journal of Advanced Manufacturing Technology. June 2003, Volume 21, Issue 9, pp 656-661.
[11] Mathivanan D., Parthasarathy N.S. “Prediction of sink depths using nonlinear modeling of injection molding variables“, The International Journal of Advanced Manufacturing Technology, August 2009, Volume 43, Issue 7-8, pp 654-663.
[12] Mathivanan D., Parthasarathy N.S., “Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm” The International Journal of Advanced Manufacturing Technology, December 2009, Volume 45, Issue 9-10, pp 867-874.
[13] Kwong C.K., Smith G.F., “A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach” The International Journal of Advanced Manufacturing Technology, 1998, Volume 14, Issue 5, pp 350-357.
[14] Shelesh-Nezhad K., Siores E. “An intelligent system for plastic injection molding process design” Journal of Materials Processing Technology, Volume 63, Issues 1–3, January 1997, Pages 458–462.
[15] DasNeogi P., Cudney E., Adekpedjou A., “Comparing the Predictive Ability of T-Method and Cobb-Douglas Production Function for Warranty Data”, ASME 2009 International Mechanical Engineering Congress and Exposition (IMECE2009) , November 13–19, 2009 , Lake Buena Vista, Florida, USA.
[16] Ribeiro B., “Support vector machines for quality monitoring in a plastic injection molding process”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Aug 2005, Volume 35, Issue 3, Pages 401 – 410.