翘曲变形是注塑制品的一种严重缺陷,而工艺参数直接影响制品的质量,因此建立翘曲与工艺参数之间的关系模型并求得最优的工艺参数对制品质量的改善非常关健.文中运用Fractional Factorial方法从众多的实脸因子中找出与塑件翘曲量密切相关的独立因子和交互因子,然后采用具有高度非线性识别能力的人工神经网络与遗传算法相结合的方法,建立塑件翘曲量与主要影响工艺参数之间的关系模型.将人工神经网络预测结果和计算机辅助工程软件模拟结果进行比较和误差分析,显示出该方法的可靠性.实验结果表明,该方法能明显提高塑件的质量,通过优化可使翘曲量减小74.06%.
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