The metal magnetic memory (MMM) method is highly effective in assessing the extent of early damage, such as fatigue crack in ferromagnetic components due to the existence of stress concentration zones (SCZs). However, there are limited studies on the relationship between the magnetic signal parameter in predicting the fatigue life of ferromagnetic components. With the advent of information relating to fatigue life, the risk of failure of a component can be reduced, if not avoided. Therefore, this study was conducted using the MMM method to establish a relationship between the magnetic signal parameters in the SCZ with fatigue characteristics in predicting the fatigue life of the specimen. A cyclic test was conducted on SAE 1045 steel specimens using a constant amplitude tension–compression stress with a stress ratio of −1. To investigate the effect of load value on the fatigue life of the specimens, seven percentage values of ultimate tensile strength (UTS) were used: 50%, 55%, 60%, 65%, 70%, 75%, and 80%. During the fatigue crack growth test, the MMM sensor was used to scan the magnetic signal data. Then, the data were analysed using the MMM Lifetime 2.0 software to obtain the fatigue life. Correlation graphs were plotted to determine the relationship between the MMM Lifetime 2.0 residual life and experimental residual life. The experimental results show that the distribution of magnetic signals was affected by the number of cycles and measurement line. As the number of cycles increased, the magnetic signal changes were more noticeable. For the measurement line, when the line was located near the crack initiation point, the magnetic signal distribution became clearer due to the presence of the SCZ. The evaluation of the maximum gradient, Kmax, variation also helped to assess the level of fatigue life based on the three stages of fatigue crack formation. Whereas, for the fatigue life assessment, the second measurement line (L2) and a contraction value of 0.7 were found to be suitable for predicting the fatigue life of specimens using MMM Lifetime 2.0 software. The experimental fatigue life and MMM Lifetime 2.0 fatigue life distribution of both parameters were in the two-factor range with the correlation coefficient, R2 of 0.9983. As such, the fatigue prediction results were acceptable. Accordingly, this study has demonstrated that the MMM method can be used to predict the remaining life of ferromagnetic components if the right parameters are used.

中文翻译：

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基于漏磁参数的钢构件疲劳寿命现象表征

金属磁记忆 (MMM) 方法在评估早期损坏的程度方面非常有效，例如由于应力集中区 (SCZ) 的存在而导致的铁磁部件疲劳裂纹。然而，关于磁信号参数在预测铁磁部件疲劳寿命方面的关系的研究有限。随着有关疲劳寿命的信息的出现，即使不能避免，也可以降低部件失效的风险。因此，本研究采用MMM方法建立SCZ中的磁信号参数与疲劳特性之间的关系，以预测试样的疲劳寿命。使用应力比为 -1 的恒幅拉压应力对 SAE 1045 钢试样进行循环试验。为了研究载荷值对试样疲劳寿命的影响，使用了极限拉伸强度 (UTS) 的七个百分比值：50%、55%、60%、65%、70%、75% 和 80%。在疲劳裂纹扩展试验中，使用 MMM 传感器扫描磁信号数据。然后，使用 MMM Lifetime 2.0 软件对数据进行分析以获得疲劳寿命。绘制相关图以确定 MMM Lifetime 2.0 剩余寿命和实验剩余寿命之间的关系。实验结果表明，磁信号的分布受循环次数和测量线的影响。随着循环次数的增加，磁信号的变化更加明显。对于测量线，当该线位于裂纹萌生点附近时，由于 SCZ 的存在，磁信号分布变得更加清晰。最大梯度 Kmax 变化的评估也有助于根据疲劳裂纹形成的三个阶段评估疲劳寿命水平。而对于疲劳寿命评估，发现第二条测量线 (L2) 和收缩值为 0.7 适合使用 MMM Lifetime 2.0 软件预测试样的疲劳寿命。两个参数的实验疲劳寿命和MMM Lifetime 2.0疲劳寿命分布均在双因素范围内，相关系数R2为0.9983。因此，疲劳预测结果是可以接受的。因此，这项研究表明，如果使用正确的参数，则 MMM 方法可用于预测铁磁部件的剩余寿命。