Optimization of plastic injection molding process via Taguchi’s parameter design method, BPNN, and DFP

Abstract

This research integrates Taguchipsilas parameter design method, back-propagation neural networks (BPNN), and Davidon-Fletcher-Powell (DFP) method to optimize the process parameter settings of plastic injection molding. Taguchipsilas parameter design method is used to arrange an orthogonal array experiment and to reduce the number of set-test cycles. Injection time, velocity pressure switch position, packing pressure, and injection velocity are selected as process control parameters and product weight is selected as the single product quality. The experimental data of Taguchipsilas parameter design method are used for effectively training and testing BPNN. Then, BPNN is combined with DFP to search out the final optimal process parameter settings. Finally, a confirmation experiment is performed to confirm the effectiveness of the final optimal process parameter settings. The experimental results show that the proposed effective process parameter optimization approach can avoid shortcomings inherent in the application of trial-and-error processes or the conventional Taguchi parameter design method. Furthermore, the proposed approach can effectively assist engineers in determining optimal initial process parameter settings and achieving competitive advantages on product quality and costs.