Machine Learning and Finite Element Simulation and Experimentation for Springback Prediction of Al-Li Alloys
Creep aging tests were conducted on the 2195 Al-Li alloys under various stress condi-tions at temperatures of 180 ℃,190 ℃,and 200 ℃ respectively.Constitutive equations were derived using MATLAB software and incorporated into the nonlinear finite element software MSC.Marc to build a finite element model for the creep aging forming of 2195 Al-Li alloy spade segments.The mod-el utilized time,stress,and temperature as input parameters,with the springback radius being the critical output parameter.To enhance the accuracy and efficiency of predictions,a comparative analy-sis of various machine learning regression models was conducted,leading to the selection of the ridge regression model as the predictive tool,which facilitated the rapid and precise prediction of the spring-back radius under diverse processing conditions.The high predictive accuracy and practical utility of the model were validated through 1:1 experimental verification,demonstrating a relative error of 0.9％between the experimental component's springback profile and the target profile.
Al-Li alloycreep aging formingmachine learningfinite element simulation
万方数据
维普
