液-固挤压复合材料系统的模糊神经网络建模

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.

Fuzzy neural network modeling of liquid-solid extrusion process for composite products

Liquid-solid extrusion process, as a method of forming tubes, bars from liquid metal in a single process, is a kind of new metal forming technology, which was developed in recent years. But there exist some problems for forming the composite tubes or bars by this process, such as the difficulty of selecting process parameters and large quantity of the experiments required. In order to deal with these existing problems, on the base of the orthogonal experiments and FEA simulation, 200 groups of samples are constructed (150 groups are used to train the network, and 50 groups are used to verify the network), and the system model for liquid-solid extrusion is established by the compensatory neurofuzzy network (CNFN). Many key techniques in the realization of CNFN learning algorithms, such as the distribution of fuzzy sets for input and output variables, the determination of fuzzy rules and learning rate, are solved. By the established model, the relation among the infiltration time and other parameters can be mapped, and the key process parameters for extruding composite bars are forecasted. The forecasted and experimental results are well matched. So the present work builds a foundation for the reasonable choosing of the process parameters and practical application of the liquid-solid extrusion.