针对液-固挤压复合材料管、棒材成形时工艺参数难于选取、试验工作量大的问题,在正交试验的基础上,结合有限元模拟数据,构建200组样本集,将其中的150组作为训练样本用于网络的训练学习,其余的50组作为测试样本用于验证网络的精确性.通过对补偿模糊神经网络学习算法实现中的关键技术问题的处理,如输入、输出变量模糊集的划分、模糊规则的提取、学习速率的确定等,基于模糊神经网络建立了液-固挤压复合材料工艺系统模型,得到了浸渗时间与其它关键参数之间的映射关系及模糊规则,利用该模型,对关键工艺参数进行预测,预测值与试验值吻合较好.这为该工艺的实际应用和过程控制奠定了基础.
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