锻压技术

基于KNN算法的中心带孔圆板拉深-翻孔变形方式的研究

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

作者：周鑫谢晖付山张清云

单位：湖南大学湖南师大附中

分类号：TG386

出版年,卷(期):页码：2021,46(7):53-59

摘要：

为了准确、快速判别中心带孔圆板拉深-翻孔过程中的变形方式，选取第3代先进超高强钢QP980材料，以预制孔直径、凹凸模圆角半径和板料厚度为特征参量，通过AutoForm成形仿真获取样本数据集，以Python作为编程语言，基于机器学习中的KNN算法构建中心带孔圆板变形方式预测分类器，并利用随机数据集验证其准确度。结果显示，当K=3、p=14且考虑距离权重时，该分类器的预测效果最佳，总体分类准确率达到90.2%，对随机数据集的分类准确率为88.9%。对比传统理论计算和计算机仿真预测，该分类器能以较高的准确度在短时间内同时预测多组样本，能够为实际生产提供参考和借鉴。

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.

基金项目：

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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