Principle strategies for the fatigue assessment of steels based on machine learning approaches

Machine learning (ML) approaches gain more and more importance for fatigue assessment of materials and industrial parts. In this work an extensive database of more than 22.000 single fatigue test and 1100 fatigue test series (SN-curves) of different steels are used to build a generalized approach for the fatigue prediction based on machine learning. For this, different strategies are used: First, on SN-curve level, the fatigue life assessment based on SN-curves, where the SN-curve parameters (slope, fatigue strength) were determined by ML and used for the fatigue life prediction later; and second, the fatigue life prediction based on specimens, where the characteristics of single specimen of the fatigue tests series (stress amplitude, roughness, hardness, …) are used (specimen level). Different ML approaches like an artificial neural network (ANN) or random forest approach are used. A higher accuracy of the direct fatigue life prediction is shown. Slightly higher accuracy was determined using an ANN. This work shows the limitation using mainly commercial, older data sources for ML-based fatigue assessment with a certain degree of inconsistency that affect the prediction accuracy of this approaches.