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材料导报  2025, Vol. 39 Issue (2): 23040250-6    https://doi.org/10.11896/cldb.23040250
  金属与金属基复合材料 |
基于磁记忆在线监测的再制造毛坯疲劳寿命预测方法
冷建成1,*, 赵雷1, 张新2, 许宏伟1
1 东北石油大学机械科学与工程学院,黑龙江 大庆 163318
2 杭州长川科技股份有限公司,杭州 310051
Fatigue Life Prediction Method of Remanufacturing Blank Based on Magnetic Memory Online Monitoring
LENG Jiancheng1,*, ZHAO Lei1, ZHANG Xin2, XU Hongwei1
1 School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318,Heilongjiang,China
2 Hangzhou Changchuan Technology Co., Ltd., Hangzhou 310051, China
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摘要 对服役构件进行在线监测并预测其疲劳寿命具有重要的工程意义。基于45钢缺口试件的拉-拉疲劳试验,利用金属磁记忆在线监测系统实时跟踪记录了试件缺口位置在整个疲劳循环过程中的磁信号变化。通过对原始监测信号进行卡尔曼滤波处理,结果表明x向和y向磁记忆信号可以将整个疲劳过程划分为三个阶段,且y向磁信号对疲劳损伤演变更加敏感;进一步引入y向磁场梯度的标准差和鞘度作为特征参数,其对应的峰值点可分别作为第一、二阶段和第二、三阶段的分界点指标。同时提出了x向磁信号突变点的峰值可用于表征试件断裂前的预警信息,并探讨了磁信号变化背后的机理,为再制造毛坯的疲劳寿命预测提供参考。
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冷建成
赵雷
张新
许宏伟
关键词:  金属磁记忆  在线监测  疲劳寿命预测  卡尔曼滤波  标准差  峭度    
Abstract: It is of great engineering significance to online monitor and predict the fatigue life of in-service components. Based on the tension-tension fatigue test of 45 steel notched specimens, the magnetic signal variation of the notched position during the whole fatigue cycle was tracked and recorded in real time by using the metal magnetic memory online monitoring system. By applying Kalman filtering to the original monitoring signals, the results show that the whole fatigue process can be divided into three stages by the x-direction and y-direction magnetic signals, and y-direction magnetic signal is more sensitive to fatigue damage evolution. Furthermore, the parameters including standard deviation and kurtosis of y-direction magnetic field gradient are introduced as characteristic parameters, and their corresponding peak points can be used as the separating indicators for the first and second stages, as well as the second and third stages, respectively. Moreover, it was proposed that the peak value of the x-direction magnetic signal can be used to characterize the pre-warning information before the fracture of the specimen, and the mechanism underlying the variation of the magnetic signal was explored, which provides a reference for predicting the fatigue life of remanufacturingblanks.
Key words:  metal magnetic memory    online monitoring    fatigue life prediction    Kalman filtering    standard deviation    kurtosis
出版日期:  2025-01-25      发布日期:  2025-01-21
ZTFLH:  TG115.28  
基金资助: 黑龙江省自然科学基金联合引导项目(LH2020E016);国家自然科学基金(11472076)
通讯作者:  *冷建成,东北石油大学机械科学与工程学院教授、博士研究生导师。目前主要从事无损检测新技术、结构健康监测与损伤诊断等方面的研究工作。lbyljc@163.com   
引用本文:    
冷建成, 赵雷, 张新, 许宏伟. 基于磁记忆在线监测的再制造毛坯疲劳寿命预测方法[J]. 材料导报, 2025, 39(2): 23040250-6.
LENG Jiancheng, ZHAO Lei, ZHANG Xin, XU Hongwei. Fatigue Life Prediction Method of Remanufacturing Blank Based on Magnetic Memory Online Monitoring. Materials Reports, 2025, 39(2): 23040250-6.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.23040250  或          https://www.mater-rep.com/CN/Y2025/V39/I2/23040250
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