| Abstract Scope |
This study applies an Integrated Computational Materials Engineering (ICME) framework to design a novel cemented carbide alloy for Laser Powder Bed Fusion (LPBF) additive manufacturing, targeting enhanced wear resistance for industries such as oil, gas, and mining. The approach integrates machine learning, multi-scale simulations, and optimization techniques to overcome challenges in incorporating more than 65% tungsten carbide (WC) while maintaining good compatibility with the LPBF process.
Multi-scale simulations, including CALPHAD, atomistic modeling, finite element analysis (FEA), and phase-field studies, were used to predict alloy behavior, optimize processing parameters, and assess microsegregation during solidification. Machine learning-driven optimization identified a low-cobalt binder composition capable of incorporating over 65% WC with a narrow solidification interval (~10 K), advantageous for LPBF. Experimental validation confirmed the alloy’s 3D-printability and improved wear resistance. This work demonstrates the effectiveness of ICME in designing high-performance materials for advanced manufacturing processes like LPBF. |