基于磁记忆的应力集中神经网络识别
Neural Network Recognition of Stress Concentration Based on Magnetic Memory Testing
摘 要
为了探索磁记忆检测技术定量表征工件应力集中程度的方法, 加工制备了不同应力集中系数的42CrMo钢试样进行拉压疲劳试验, 采用磁记忆检测仪器测量不同疲劳周次时试样表面的法向和切向磁记忆信号。确定了不同应力集中程度下磁记忆信号的特征参量, 并以此作为输入特征向量建立了BP神经网络, 对试样的应力集中程度进行定量识别。结果表明: 利用建立的BP神经网络能够实现试样应力集中程度的定量识别。
磁记忆信号
应力集中
特征提取
BP神经网络
metal magnetic memory testing
magnetic memory signal
stress concentration
feature extraction
BP neural network
Abstract
To explore a properly method for characterizing stress concentration degree quantitatively by metal magnetic memory testing (MMMT), tension-compression fatigue tests of specimens with different stress concentration factors made of 42CrMo steel were carried out. Both normal and tangential component of magnetic memory signals of specimens under different fatigue cycles were measured by magnetic memory apparatus. A back propagation neural network (BP neural network) was built to distinguish the stress concentration degree, whose input eigenvector was the feature extracted from magnetic memory signals. The results showed that the BP neural network could be used to recognize stress concentration degree of specimens quantitatively.
中图分类号 TG115.28+4
所属栏目 试验与研究
基金项目 国家自然科学基金项目(50975283, 50975287); 国家973课题(2011CB013401); 装备维修改革项目(2011WG07)
收稿日期 2013/1/8
修改稿日期
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备注王慧鹏(1983-), 男, 博士研究生。
引用该论文: WANG Hui-peng,DONG Li-hong,DONG Shi-yun,XU Bin-shi. Neural Network Recognition of Stress Concentration Based on Magnetic Memory Testing[J]. Physical Testing and Chemical Analysis part A:Physical Testing, 2013, 49(9): 576～579
王慧鹏,董丽虹,董世运,徐滨士. 基于磁记忆的应力集中神经网络识别[J]. 理化检验－物理分册, 2013, 49(9): 576～579
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参考文献
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【3】DUBOV A A. Express method of quality control of a spot resistance welding with usage of metal magnetic memory[J].Welding in the World,2002,46:317-320.
【4】DONG Li-hong, XU Bin-shi, DONG Shi-yun, et al. Variation of stress-induced magnetic signals during tensile testing of ferromagnetic steels[J].NDT & E International,2008,41:184-189.
【5】SHI Chang-liang, DONG Shi-yun, XU Bin-shi, et al. Stress concentration degree affects spontaneous magnetic signals of ferromagnetic steel under dynamic tension load[J].NDT & E International,2010,43:8-12.
【6】董丽虹,董世运,徐滨士.金属磁记忆技术表征应力集中、残余应力及缺陷的探讨[J].材料工程,2009, 8:19-23.
【7】DONG Li-hong, XU Bin-shi, DONG Shi-yun, et al. Study on the magnetic memory signals of medium carbon steel specimens with surface crack precut during loading process[J].Rare metals, 2006,25(Spec):431-435.
【8】DONG Li-hong, XU Bin-Shi, DONG Shi-yun, et al. Monitoring fatigue crack propagation of ferromagnetic materials with spontaneous abnormal magnetic signals[J].International Journal of Fatigue,2008,30:1599-1605.
【9】徐海波,樊建春,张来斌,等.钻具试样的磁记忆检测技术研究[J].无损检测,2008,30(5):301-303.
【10】丁小军,李福泉,李家平,等.高压管汇的磁记忆检测[J].无损检测,2008,30(9):660-661.
【11】MIGUEL Rocha, PAULO Cortez, JOSE′ Neves. Evolution of neural networks for classification and regression[J].Neurocomputing,2007,70:2809-2816.
【12】赖静,王清,孙东立.人工神经网络在材料研究中的应用[J].材料工程,2006(z1):458-462.
【13】周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京: 清华大学出版社,2005.
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