Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods

摘要

There was studied a jump-like deformation of AMg6 aluminum alloy at static tensile test in the soft mode of loading. The experimental methods of jump-like deformation study of this alloy are often complicated, expensive and time-consuming. Therefore, it is more effective to model the stress-strain diagram numerically. One of the promising ways to predict the stress-strain diagram of AMg6 material is based on the application of machine learning methods, in particular by neural networks, boosted trees, support-vector machines, and k - nearest neighbors. It was discovered that the method of neural networks gives the least prediction error equal to 6.9%. The errors of the methods of boosted trees, support-vector machines and k - nearest neighbours are 9.1, 7.7, and 9.4% in test sets, respectively.