Determination of hardness and Young's modulus in fcc Cu-Ni-Sn-Al alloys via high-throughput experiments, CALPHAD approach and machine learning

Hardness and Young's modulus are critical indicators in the design of innovative Cu-Ni-Sn-Al alloys with desired elastic and strength properties. In this study, the composition-dependent hardness and Young's modulus in the fcc Cu-Ni-Sn-Al alloys were determined using high-throughput experiments, the CALPHAD (CALculation of PHAse Diagrams) approach, and machine learning (ML) model. By combining the diffusion-couples tool, nanoindentation, and electron probe microanalysis (EPMA) techniques, the 341 sets of composition-related hardness and Young's modulus in the fcc Cu-Ni-Sn-Al system, as well as its sub-binary and sub-ternary systems, were effectively determined. These measured data were subsequently utilized to establish the relationship of Young's modulus and hardness with respect to composition in the fcc Cu-Ni-Sn-Al system through the combined application of CALPHAD and back propagation (BP) artificial neural network machine learning models. A comparison between the two approaches revealed that the ML method achieves higher accuracy in ternary and quaternary alloys compared to the CALPHAD method. The impact of alloying elements on the values of hardness and Young's modulus was analyzed, demonstrating that the Ni content has the largest effect on hardness, while the Young's modulus increases with addition of Ni but decreases with the addition of Al and Sn. Finally, the effects of solute elements on the nanomechanical properties of aged Cu1-x(Ni3Sn)x and Cu-9Ni-6SnxAl alloys were examined.