Eco-design of sustainable high entropy shape memory alloys: combinatorial and data-driven exploration – ECO-SHEAP

Shape memory alloys (SMAs) are a family of smart materials characterised by remarkable properties such as superelasticity, shape memory or damping. However, these alloys are prone to functional thermal fatigue and thermal ageing problems. One way to overcome these problems is to develop high entropy shape memory alloys (HESMA). The first HESMAs to be developed are derived from the NiTi alloy family and have greatly improved the thermal fatigue and transformation temperatures of the reference binary alloy. However, they are composed of substitution metals defined by the European Union as 'critical raw materials'.

In this context, the aim of this project is to develop an alternative class of copper-based HESMAs with properties comparable to NiTi alloys while containing less critical and more sustainable elements. A combinatorial exploration of the chemical composition space will be carried out by combining data mining, thermodynamic calculations and high-throughput synthesis of HESMAs by additive manufacturing. Artificial intelligence algorithms will then be developed to relate the chemical composition of alloys and their heat treatment to the nature of the phases, their thermal stability and martensitic transformation temperatures.

A multi-objective function algorithm will be proposed for the design and development of alloys that must satisfy the following criteria: a single-phase metallurgical state, high transformation temperatures, thermal stability and economic, environmental and societal sustainability criteria. Multi-scale (XRD, SEM, EDS) and thermomechanical (DSC, (an-)isothermal mechanical tests, ageing) characterisation of these alloys will then be carried out to define the stability of the shape memory effect as a function of the metallurgical state.