Rapid Data Acquisition System for Complex Algorithm Testing in Plastic Molding Industry
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.
<p>[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.</p>
<p>[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.</p>
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:56193", author = "A. Tellaeche and R. Arana", title = "Rapid Data Acquisition System for Complex Algorithm Testing in Plastic Molding Industry", 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.", volume = "7", number = "7", pages = "1548-5", }
