Modelling of AL-6061 aluminum alloy deformation diagrams by machine learning methods

There was studied a deformation diagrams of AL-6061 aluminum alloy at various temperatures. Predicting the behavior of nonlinear systems is important. Experimental data have a certain variance that must be taken into account. This gives us a regression problem that can be solved by machine learning. Therefore, with sufficient experimental data, it is advisable to use machine learning methods, namely neural networks and boosted trees in science and technology, where stress-strain diagrams of structural materials is extremely important, in particular in metallurgy, aircraft, railways and more. The diagrams of AL-6061 aluminum alloy deformation was predicted by machine learning methods, in particular, neural networks and boosted trees at temperatures T = 343, 413 degrees C. It is shown that the obtained results are in good agreement with the experimental ones. It was founded that the neural network method gives the least prediction error of 0.05% in the test sample. (c) 2022 The Authors. Published by Elsevier B.V.