A Fuzzy Rule-based Network Model for Optimization of Process Parameters in Plastic Injection Molding

Injection molding is the most crucial process for forming plastic products, and process parameters are one of the critical factors affecting the appearance, size, and performance of products. However, the optimization of the process parameters is weakly theoretical and strongly empirical problems, and there is an urgent need to develop a scientific and systematic method. In response to the problem of the strong dependence of manual experience in product defect correction, a unified knowledge form using fuzzy rules was constructed and a Takagi-Sugeno-Kang (TSK) fuzzy rule network model integrating knowledge representation and inference of process parameters optimization was established. Furthermore, a learning method was proposed to automatically discover optimization rules of process parameters from process datasets. The Dropout strategy and Bagging ensemble learning strategy were adopted to alleviate the problem of rule explosion caused by the growth of process knowledge bases in high-dimensional process data. Then, the influences of the fuzzy rule network parameters and structure on knowledge representation and inference were analyzed. Based on these analyses, two methods, parameters learning and structure learning of the model were developed respectively. The learning method of process data based on experience pool replay was proposed, establishing the incremental learning strategy of process data. The test results on the injection molding dataset showed that the number and length of rules were reduced by 50% in the proposed fuzzy rule-base network model, realizing high interpretability as well as incremental learning stability. © 2022 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.