Transactions of Materials and Heat Treatment

Title：Study on drawing-flanging deformation mode in circular plate with center-hole based on KNN algorithm

Authors： Zhou Xin Xie Hui Fu Shan Zhang Qingyun

Unit： Hunan University The High School Attached to Hunan Normal University

KeyWords： compound forming deformation mode KNN algorithm machine learning predictive classifier

ClassificationCode：TG386

year,vol(issue):pagenumber：2021,46(7):53-59

Abstract：

In order to discriminate the deformation mode of the circular plate with center-hole in the drawing-flanging process accurately and quickly, for the third-generation advanced high strength steel QP980 material, taking diameter of pre-manufactured hole, fillet radius of punch and die and sheet thickness as characteristic parameters, the sample datasets were obtained by forming simulation software AutoForm, and Python was used as the programming language. Then, based on KNN algorithm in machine learning, a deformation mode predictive classifier of the circular plate with center-hole was built, and its accuracy was verified by a random datasets. The results show that when K=3, p=14 and the distance weight are considered, the classifier has the best prediction effect, the overall classification accuracy reaches 90.2%, and the classification accuracy of the random datasets is still 88.9%. Compared with the traditional method of theoretical calculation and the computer simulation prediction, the classifier is able to simultaneously predict multiple groups of samples in a short time with high accuracy, which can offer experience and reference for actual production.

Funds：

国家重点研发计划（2017YFB0304400）

作者简介：周鑫（1995-），男，硕士研究生，E-mail：644916184@qq.com；通信作者：谢晖（1971-），男，博士，教授，E-mail：danielxie@163.com

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