Machine learning based short fatigue crack growth rate prediction for aluminium alloys

Abstract

As a result of different fatigue characteristics influenced by intricate microstructures, comparing with fatigue crack growth rate in long fatigue crack region, the growth in the short one is more complex to be fitted with fewer parameters. There have been more restrictions for traditional models in describing the nonlinearity between the fatigue crack growth rate and stress intensity factor range in short crack regime. Due to their outstanding ability in prediction with high accuracy and in description of nonlinearity with satisfactory flexibility, machine learning approaches have been payed more attention. The machine learning models have been the better choices to deal with the limitation in fatigue-related problems which traditional solutions cannot overcome. In this paper, two machine learning algorithms: k-nearest neighbour algorithm (KNN) and random forest (RF) are implemented to predict the short fatigue crack growth rate for 2024-T3 and LC9cs aluminium alloys. The testing outcomes of these applied machine learning algorithms are compared to evaluate their prediction abilities. The final results reveal that the values of Pearson correlation coefficient R2 of the KNN are generally higher than that of another method for each material. Each of them has an excellent performance in accuracy and effectiveness, and all of them have excellent extrapolation capabilities to predict the nonlinearity.