A Fast Identification Method of Yield Strength of Materials Based on Bending Experimental Data

Author:

Duan Yongchuan,Tian Le,Zhang Fangfang,Qiao Haidi,Li Muyu,Yang Liu,Guan Yingping

Abstract

Identifying the yield strength of materials quickly and accurately is the key to realizing defect prediction and digital process control on the production line. This paper focuses on identifying the material yield strength based on bending deformation, analyzing the influence of different die fillets, punch fillets, and die spans on the curve shapes, determining the reasonable dimensions of the device, and developing them. Two methods for rapidly extracting the yield load are proposed—the window vector method (WV) and the fitting residual method (FR)—and compared with the double secant line method (CWA) and the one tenth thickness method (t/10). Because there is no direct correspondence between the yield load and the material performance parameters, the relevant equations were fitted using the experimental data. The linear correlation between load and yield strength determined by these four methods was close to 0.99. Finally, four kinds of sheets with high, medium and low yield strength were tested and compared with the observed results. The result shows that when the yield strength is small, the average error and the relevant model dispersion will increase. As the yield strength increases, the biases increase gradually. The prediction errors based on the t/10, WV, and FR methods were all below 4%.

Funder

Natural Science Foundation of Hebei Province

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Materials Science,Metals and Alloys

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Precision and Flexible Bending Control Strategy Based on Analytical Models and Data Models;Chinese Journal of Mechanical Engineering;2022-08-12

2. Assessment of Materials Selection Problem in Cryogenic Tank Using COPRAS Method;Data Analytics and Artificial Intelligence;2021-03-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3