abstract
- © 2022 by the authors.Lightweight materials are in constant progress due to the new requirements of mobility. At the same time, it is mandatory to meet the internal standards of the original equipment manufacturers to guarantee product quality, and market regulations are necessary to reduce or eliminate pollution emissions. In order to reach these technical requirements, the design is optimized, and new materials and alloys are evaluated. The search for these new types of materials is long and expensive. For this search, new technologies have emerged, such as integrated computational materials engineering, which is a valuable tool to forecast through simulation alloy characteristics that meet specific requirements without fabrication. This research develops an artificial neural network to establish the chemical composition of a new aluminum alloy based on the desired manufacturing characteristics as well as fatigue strength. For this, the proposed artificial neural network was trained with the chemical composition of preexisting aluminum-based alloys and the resulting desired mechanical properties. The significant contribution of the proposed research consists not only of the neural network high-performance forecasting but also the fact that for to train and validate it, not only simulations of its responses to the different possibilities of alloys were tried but also validated through an experimental laboratory test performed by uniaxial machine. The proposed artificial neural network results show an average correlation of 99.33% between its forecasting and laboratory testing.