屈服强度在线识别的分散性分析与精度验证

doi:10.3969/j.issn.1004-132X.2020.23.011

中国机械工程

屈服强度在线识别的分散性分析与精度验证

段永川;乔海棣;张芳芳;刘煜;杨柳;官英平

燕山大学机械工程学院，秦皇岛，066004

出版日期:2020-12-10 发布日期:2020-12-18
基金资助:
国家自然科学基金资助项目（51705448）；
河北省自然科学基金资助项目（E2018203100）；
燕山大学博士基金资助项目（B861）；
燕山大学青年教师独立研究基金资助项目（15LGB002）

Dispersive Analysis and Accaracy Verification of Yield Strength Online Recognition

DUAN Yongchuan;QIAO Haidi;ZHANG Fangfang;LIU Yu;YANG Liu;GUAN Yingping

School of Mechanical Engineering,Yanshan University, Qinhuangdao, Heibei, 066004

Online:2020-12-10 Published:2020-12-18

摘要/Abstract

摘要： 构造了窗口向量法和标准化拟合残差法用于在线识别板料的屈服参数，并给出了屈服响应显著程度的一种评价方法，从几何角度分析了模具尺寸对屈服识别分散性的影响机理。窗口向量法效果更好，可消除不同硬化指数带来的屈服识别分散性。所提出的两种方法都使用了拟合算法并具有一定的抗噪声干扰能力。基于窗口法预测出的真实材料屈服强度相对误差在15%以内。

关键词: 自由弯曲, 标准化拟合残差法, 窗口向量法, 屈服强度, 分散性分析

Abstract: Window vector method and standardized fitting residual method were constructed to identify yield parameters of sheet metals on line, and a yield response significance evaluation method was given. Influence mechanism of die sizes on yield recognition dispersion was analyzed from geometric point of view. Window vector method has better effectiveness and may eliminate dispersion of yield recognition brought by different hardening indices. The proposed two methods both use fitting algorithm and have certain anti-noise interference ability. Relative errors of real material yield strength which is predicted based on the window method are within 15%.

Key words: air bending, standardized fitting residual method, window vector method, yield strength, dispersive analysis

中图分类号:

TG386.31

段永川;乔海棣;张芳芳;刘煜;杨柳;官英平. 屈服强度在线识别的分散性分析与精度验证[J]. 中国机械工程, DOI: 10.3969/j.issn.1004-132X.2020.23.011.

DUAN Yongchuan;QIAO Haidi;ZHANG Fangfang;LIU Yu;YANG Liu;GUAN Yingping. Dispersive Analysis and Accaracy Verification of Yield Strength Online Recognition[J]. China Mechanical Engineering, DOI: 10.3969/j.issn.1004-132X.2020.23.011.

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屈服强度在线识别的分散性分析与精度验证

Dispersive Analysis and Accaracy Verification of Yield Strength Online Recognition

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