An online wall thickness control strategy for the extrusion blow molded part was proposed in this work. A simulation-based optimization method combining with finite element, artificial neural network, and genetic algorithm was used to determine the initial die gap profile for a part with required thickness distribution. A multi-channel ultrasonic thickness measurement system was built up to get the in-mold wall thickness of the blow molded part. Then, a feedback closed-loop control system based on fuzzy iterative learning control algorithm was designed and implemented to control the wall thickness of blow molded part. The results showed that the online wall thickness control system developed in this work can automatically achieve a proper die gap profile and get the satisfied part thickness distribution.
A design of lterative learning control system and its application to a batch polymerization reactor.
