Finding an Optimal Operation Conditions of Plastic Molding by Artificial Neural Network.

Yonehara, Noriyoshi; Ito, Ikuo

doi:10.1299/kikaic.63.3538

Cited by 1 publication

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“…Optimization relying on numerical methods has been explored as a means to increase the efficiency in polymer processing. The underlying models used in optimization can be of varying forms including response surfaces , neural networks , fuzzy sets , and others. The objectives of the optimization can also vary widely.…”

Section: Introductionmentioning

confidence: 99%

On‐line multivariate optimization of injection molding

Johnston

McCready

Hazen

et al. 2015

Polymer Engineering & Sci

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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 online 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

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Section: Introductionmentioning

confidence: 99%