Injection Molding Process Optimization of Multi-Objective Based on MUD-RBFNN-GA
The optimization algorithm of MUD-RBFNN-GA was proposed in this article. An injection molding process optimization model of multi-factor and multi-objective was also researched. The multiple uniform designs of experiment was applied to optimize the processing parameters. During this process, the RBF neural network was established, where the melt temperature, mold temperature and packing pressure were taken as the inputs, and warpage, area of air-traps and weld-line length as the outputs, and the Moldflow simulation analysis was used to obtain the output values. By combining the algorithm with genetic algorithm and global optimization in the networks, we can get the optimal process parameters. The results show that the multi-objective optimization based on MUD-RBFNN-GA is practically applicable, and it can reduce the molding defects effectively.
Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng
B. S. Sun et al., "Injection Molding Process Optimization of Multi-Objective Based on MUD-RBFNN-GA", Applied Mechanics and Materials, Vols. 37-38, pp. 564-569, 2010