根据经实验验证的玻璃钢(GFRP)拉挤工艺过程数学模型,以数值模拟结果为样本数据,建立反向传播(BP)神经网络,得到拉挤工艺参数(固化温度、拉挤速度)与GFRP固化度间非线性相关关系.采用神经网络结合带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)解决拉挤过程中固化炉温度和拉挤速度多目标优化问题,得到了拉挤优化问题的Pareto最优解集.实验结果表明,优化后的工艺参数能有效提高生产率,降低固化炉温度,效果显著.
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