关键词: 薄壁件, 田口法, 遗传算法, 工艺参数优化

Abstract: Combining with advantages of neural network method and genetic algorithm, a method to optimize machining process parameters was proposed for thin-walled workpieces based on back propagation neural network(BPNN). The data gained from Taguchi experiments were applied to train in BPNN so as to generate the S/N ratio predictor and quality predictor. By maximizing the S/N ratio, variation of milling processes was minimized, and the optimal process parameter combinations were found. Through numerical simulation and machining experiments, effectiveness of the proposed method in optimization of milling process parameters of thin-walled workpieces was validated.

Key words: thin-walled workpiece, taguchi method, genetic algorithm, processing parameter optimization