基于磁记忆在线监测的再制造毛坯疲劳寿命预测方法

doi:10.11896/cldb.23040250

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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

Published: 25 January 2025 Online: 2025-01-21

CLC number:

TG115.28

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	Articles by authors
	LENG Jiancheng
	ZHAO Lei
	ZHANG Xin
	XU Hongwei

Cite this article:

LENG Jiancheng,ZHAO Lei,ZHANG Xin, et al. Fatigue Life Prediction Method of Remanufacturing Blank Based on Magnetic Memory Online Monitoring[J]. Materials Reports, 2025, 39(2): 23040250-6.

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https://www.mater-rep.com/EN/10.11896/cldb.23040250 OR https://www.mater-rep.com/EN/Y2025/V39/I2/23040250

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