| Abstract Scope |
Cu-Ni-Cr alloys are prized for their strength, corrosion resistance, and adaptability in demanding sectors like aerospace and energy. This study applies an Integrated Computational Material Engineering (ICME) approach, combining molecular dynamics (MD) simulations, finite element analysis (FEA), and machine learning (ML), to discover alloy compositions with optimized mechanical properties. First, MD simulations calculate key properties—elastic modulus, ultimate tensile strength, bulk modulus, shear modulus, and Poisson’s ratio—across various compositions, validated against experimental trends. These data feed into the macroscale FEA for tensile and bending tests, simulating performance under realistic loads. Using these results as optimization objectives, ML identifies compositions predicted to offer superior mechanical balance. This ICME approach significantly accelerates alloy discovery by reducing dependence on physical testing, enabling rapid optimization across a broad compositional space. The findings advance materials design for high-performance applications, providing a scalable framework adaptable to diverse alloy systems. |