Simulating the future of manufacturing. My research harnesses the power of Multiphysics to create digital twins of advanced processes like laser cladding and additive manufacturing. I develop high-fidelity models to predict thermal stress, melt pool dynamics, and microstructure evolution, specifically for depositing novel High-Entropy Alloys (HEAs). This computational approach allows me to solve complex engineering challenges and optimize the creation of next-generation, high-performance materials and components. 

I use JMatPro to predict temperature-dependent thermophysical properties and generate CALPHAD-based phase diagrams for customized alloys. My work extracts key inputs — thermal conductivity, specific heat, density, melting/solidification ranges and other temperature-dependent properties — to feed process and microstructure simulations and to interpret experiments. I apply these tools to novel materials, especially high-entropy alloys (HEAs) and functionally graded materials (FGMs), enabling rapid evaluation of composition–temperature spaces and guiding alloy design. These computational predictions are integrated with phase-field and microstructural analyses to support parameter selection in additive manufacturing and to optimise surface-coating strategies. 

Additive manufacturing combines process development, materials science, and quality assurance to fabricate complex parts layer-by-layer. Important elements are process technologies (laser cladding, directed energy deposition, powder-bed fusion), process-structure-property studies, parameter optimization and design of experiments, powder feedstock development (including HEAs), in-situ monitoring and control, and post-processing (heat treatment, machining, surface finishing). The focus is on achieving repeatable microstructures, tailored mechanical/tribological performance, and scalable, industry-ready manufacturing routes.