基于GA-BP混合算法的液固挤压工艺组合参数逆向设计

为改善液固挤压复合材料成形过程中金属的流动均匀性,减少制件的内部损伤缺陷,基于人工神经网络及遗传算法,采用改进的混合GA-BP算法建立了设计参数与控制目标的非线性映射关系。通过对样本集的学习,初步建立了液固挤压工艺组合参数知识库,将网络预测值与实验值进行对比,其最大相对误差不超过0.79%,说明采用GA-BP混合算法建立的预测模型具有较高的预测精度。利用所建立的预测模型,分析了模具参数和工艺参数组合对制件变形均匀性的耦合作用,为液固挤压工艺的综合设计与优化提供了理论依据。

Reversing design on combined parameters of liquid-solid extrusion process based on the predictive model using hybrid GA-BP algorithm

In order to control deformation uniformity of composite in the forming process of liquid-solid extrusion and reduce inner damaging defects of products. Based on artificial neural network (ANN) technique and genetic algorithm (GA), the nonlinear mapping relation between design variables and objective function was proposed and established by the modified GA-BP algorithm. The simulation results of FEM called virtual samples were selected as the network's training samples. By training the sample, the knowledge base of the muti-parameters for the liquid-solid extrusion was set up. Comparing with the experimental results, the largest relative error between the actual output value of the network and the experimental data is 0. 79 percent It proves that the forecast model established using GA-BP hybrid algorithm has a higher accuracy. The influences of main process parameters and structures parameters had been studied on the deformation uniformity using the predictive function of the model. They are good instructions for the design and optimization of the liquid-solid extruding composites process.