Thermodynamic Analysis of High-Strength Al-Cu-Mg-Si Alloys at High Temperatures Designed by Using Bayesian Learning for Neural Networks

We discuss the design of 2000 series high-strength aluminum alloys at high temperatures using Bayesian learning for neural networks and thermodynamic analysis. It is known that the strength of aluminum alloys decreases rapidly above 150°C, so improving the strength at high temperatures is essential for industrial applications. In order to design high-strength alloys, it is necessary to optimize the additive element compositions and the heat treatment conditions such as temperature and time for homogenization, solution processing, and aging. A data science approach using neural networks is suitable for handling such multi-dimensional problems and exploring the optimal process conditions from the vast design space. This study focuses on the thermodynamic calculations of the behavior of the size and dispersion of precipitates affecting the high-temperature strengthening mechanism of designed alloys by the neural network.