Construction of a 3C Product Mold Manufacturing Process Predictive Optimization Model

Abstract

Mold trials are critical in the mold development process; therefore, it is necessary to develop predictive models that can control processing results and solve problems in processing parameter optimization to ensure manufacturing efficiency and processing quality. Using the six sigma method, this research constructed an optimized 3C (Computer, Communication, and Consumer electronic) product mold manufacturing process predictive model, and conducted an empirical study on the largest electronic products foundry. The Taguchi parameter design method, the back propagation network (BPN) prediction method, and genetic algorithms (GAs) were used to establish an optimization search module. The surface quality was inferred by the network predictive model as a limiting condition for acquiring the maximized material removal rate in milling. The optimized milling processing parameters of the maximum fitness degree can be determined by GA. The findings can serve as a practical reference for quality improvements and decision planning.

Keywords:

• 3C product
• Back propagation network (BPN)
• Genetic algorithm (GA)
• Six sigma method