Optimization method of process parameters of composite molding

doi:10.3969/j.issn.1000-1565.2025.01.011

Abstract

Abstract: Aiming at the problems of high energy consumption, low production efficiency and unstable product quality of traditional molding process, the adaptive genetic algorithm was proposed to optimize the multi-parameters of holding time, molding pressure and temperature in the molding process. And the molding pressure, holding time and temperature of process data obtained by experiments are taken as input layer neurons, and the warping deformation of molding mass is taken as output layer neurons. BP neural network was constructed to obtain the functional relationship between warping deformation and molding pressure, holding time and temperature. Then adaptive genetic algorithm is used to optimize multiple process parameters. After the steps of binary coding, selection, crossover and mutation, the optimized results were obtained. The results show that the adaptive genetic algorithm can improve the warping deformation caused by the imbalance of holding time, molding pressure and temperature in the molding process, and improve the molding quality of the product.

Key words: molding, process parameters, multi-parameter optimization, adaptive genetic algorithm

CLC Number:

TP311

YANG Zeqing,DU Jingxuan,HU Ning,ZHANG Yanxing,JIN Yi. Optimization method of process parameters of composite molding[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(1): 104-112.

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