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On‐line multivariate optimization of injection molding
Author(s) -
Johnston Stephen,
McCready Christopher,
Hazen Daniel,
VanDerwalker Darin,
Kazmer David
Publication year - 2015
Publication title -
polymer engineering and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 111
eISSN - 1548-2634
pISSN - 0032-3888
DOI - 10.1002/pen.24163
Subject(s) - controller (irrigation) , process (computing) , computer science , molding (decorative) , process control , control theory (sociology) , transfer molding , mechanical engineering , materials science , engineering , control (management) , artificial intelligence , mold , agronomy , composite material , biology , operating system
An auxiliary process controller was designed, implemented, and validated for on‐line process and quality optimization. The objective function included terms related to the process variation, model uncertainty, and control energy. The controller architecture relied on characterized models including both process transfer functions and principal components analysis to perform on‐line optimization in parallel with the physical molding process. New process and quality observations were input to the controller to update the models and provided new settings for the machine controller. Experimentation included characterization with a D‐optimal design of experiments followed by a validation to measure the controller's performance with respect to controller stability, extrinsic material variation, cycle time reduction, and other common manufacturing goals. In every case, the controller was able to reduce the value of the objective function while also improving the part dimensions relative to tight tolerance specifications. While characterization experiments could be costly, the use of the resulting process models greatly speeds convergence and facilitates the consideration of various cost and quality terms in the objective function. POLYM. ENG. SCI., 55:2743–2750, 2015. © 2015 Society of Plastics Engineers