The basis for a novel pattern-based closed-loop control strategy for the injection molding process is presented. The strategy uses artificial neural networks (ANNs) embedded within a cascade design to analyze sensor patterns, identify process character and control part quality. The platform for this work, the injection molding process, is an industrially significant, cyclic manufacturing operation. Final part quality of this process is a nonlinear function of many machine and polymer variables. Part quality control of this process is currently attained via single input-single output machine controls supervised by human operators. Presented here is a method that employs ANN technology to improve upon this approach and provide the basis for closed-loop part quality control. In the cascade design, machine controller set-points of an inner loop are updated based on ANN analysis of mold cavity pressure patterns. The controller action maintains the desired pressure pattern set-point of the outer loop associated with desired part quality. Control strategy details are provided along with set-point tracking demonstrations that support feasibility of this pattern-based approach.
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