Rationalisation of aluminium alloys using machine learning and Artificial Intelligence

Abstract

Aluminium alloys are widely used across various sectors of engineering due to their lower density combined with higher strength compared to many existing alloys of other metals. These unique characteristics have led to an increased demand for and discovery of new aluminium alloys with targeted properties and compositions. Traditional methods of designing new mate-rials with desired properties, such as trial-and-error and reliance on domain experts' experience, are time-consuming and expensive. These techniques also expand the search area for suitable alloys. In this research, we propose a machine learning-based design system to reduce the number of grades across all series of age-hardenable and non-age-hardenable aluminium alloys. The sys-tem collects features based on chemical composition, mechanical properties, corrosion re-sistance, weldability, and thermal and electrical properties under different tempering and hard-ening conditions for machine learning modelling. A combination of PCA (Principal Compo-nent Analysis) and K-means clustering is applied for clustering and sub-clustering similar al-loys based on their compositional and property profiles into clusters and sub-clusters. Next, an optimisation algorithm, namely a multi-property decision-making method, i.e., TOPSIS (Tech-nique for Order Preference by Similarity to Ideal Solution), identifies the optimum alloys within each sub-cluster. These selected alloys exhibit a balanced set of properties that effec-tively represent the range of characteristics found among other alloys in the same sub-cluster. Subsequently, a recycling algorithm is applied to predict the mixing ratio based on closeness scores generated by the optimisation algorithm. This process mixes the optimum alloy in each sub-cluster with the remaining alloys in the sub-cluster, resulting in a single optimised alloy as determined by the optimisation algorithm. This method significantly reduces the number of alloy grades while maintaining key material properties and enhancing recyclability, which has a metallurgical basis. This design system is enhanced and developed into a dedicated recycling software application, offering a practical tool for the aluminium industry. It supports sustainable development and improves recycling efficiency, aligning alloy manufacturing with the principles of the circular economy.