Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning

By the methods of machine learning (neural networks, boosted trees, random forests, support-vector machines, and k -nearest neighbors), we plotted the diagrams of fatigue fracture of D16T aluminum alloy under regular loading with a stress ratio R = 0, 0.2, 0.4, and 0.6. The obtained results are in good agreement with the experimental data. It was discovered that the method of neural networks gives the least prediction error equal to 3.2 and 2.5% in tested samples.