CAJ | 학술논문

为改善液周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.
위개선액주제압복합재료성형과정중금속적류동균균성,감소제건적내부손상결함,기우인공신경망락급유전산법,채용개진적혼합GA-BP산법건립료설계삼수여공제목표적비선성영사관계.통과대양본집적학습,초보건립료액고제압공예조합삼수지식고,장망락예측치여실험치진행대비,기최대상대오차불초과0.79%,설명채용GA-BP혼합산법건립적예측모형구유교고적예측정도.이용소건립적예측모형,분석료모구삼수화공예삼수조합대제건변형균균성적우합작용,위액고제압공예적종합설계여우화제공료이론의거.
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.