Machine learning guided alloy design and thermomechanical process optimisation for high performance automotive aluminium alloys

Traditional intuition-based and trial-and-error experimentation used in the development of new alloys and/or optimisation of processing conditions are very time-consuming and costly approaches. Materials modelling and process simulations guided by physics-based principles have helped to reduce the time and cost to some extend in the search for optimal solutions in terms of alloy composition and processing parameters for a given metallic material that meets the technical requirements of specific engineering applications. However, the increasing complexity of alloy chemistry and materials processing methodologies can be challenging to current multiphysics-based computational modelling due to the lack of quantitative phenomenological relationships between the composition/processing and properties of alloys. The power of machine learning (ML) is that it can operate on high-dimensional data to make sense of the complexity of multi-element alloy compositions and multi-step processing. The data-driven models generated by machine learning approach have been used extensively in the materials engineering domain to successfully find such complex correlations leading to effective materials design(1-6).