Transactions of Materials and Heat Treatment

Title：Prediction on roll forging process parameters of front axle based on machine learning

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ClassificationCode：TG316

year,vol(issue):pagenumber：2021,46(1):208-214

Abstract：

The roll forging process parameters of the first pass for front axle were predicted by the least square support vector machine algorithm based on the mixed kernel function, and the mixed function was constructed to improve the prediction accuracy of predicted model. Then, the process parameter predicted model was verified by experiment. The results show that compared with LS-SVM algorithm based on the single RBF kernel, the predicted model constructed by the mixed kernel function LS-SVM algorithm has higher prediction accuracy, and the average prediction accuracies of three groups of predicted models with different kernel function parameters for the maximum forming load and widening are 91.8% and 92.1%, 89.4% and 90.1%, 94.5% and 93.2% respectively. Furthermore, the optimal kernel function parameters are determined with RBF kernel function coefficient γ=4.52, penalty coefficient c=276.4, Polynomial kernel function kernel order q=1.21 and mixed weight coefficient a=0.28. Finally, the feasibility of prediction method for the studied front axle roll forging process parameters was verified by three groups of process parameter experiments, and the errors between the predicted maximum forming load and widening by the prediction method and the actual values are less than 10%.

Funds：

基金项目:吉林省重点科技研发项目(20180201073SF);吉林省科技发展计划资助项目(20190902010TC)

作者简介：董玮（1985-），女，硕士，讲师 E-mail：zishang1999@126.com 通讯作者：陈桂芬(1956-)，女，博士，教授，博士生导师 E-mail：guifchen@163.com

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