On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets

In this research a machine learning model for predicting the rotating bending fatigue strength and the high-throughput design of fatigue resistant steels is proposed. In this transfer prediction framework, machine learning models are first trained to estimate tensile properties (yield strength, tensile strength and elongation) on the basis of composition and critical process conditions. Then, based on the predicted tensile properties, transfer models are trained to estimate fatigue strength. The results are compared with those of a similar model not having such a transfer layer. The transfer prediction framework shows high accuracy for fatigue strength prediction with a remarkably high tolerance to limitations in the amount of calibration data available for training. By combining the transfer prediction framework with evolutionary algorithms, a robust high-throughput alloy design model is achieved requiring only tens of fatigue data points to get a decent reliability. The newly designed steel showed the predicted high fatigue strength. The method as presented here might also be applicable to other alloy design challenges in which only a limited database for the property to be optimized is available.