Computational Materials Research Group

Machine learning is becoming increasingly important in the numerical modeling of solid-state additive manufacturing (SSAM) processes like Wire Arc Additive Manufacturing (WAAM) and Friction Stir Based Additive Manufacturing. This is because SSAM involves complex, dynamic interactions between various process parameters (e.g., tool speed, force, geometry), material properties (e.g., flow stress, thermal conductivity), and the resulting microstructure and mechanical properties. Traditional numerical models often struggle to accurately capture these interactions due to the challenges in modeling material behavior at high strain rates and temperatures, complex geometries, and evolving microstructures. We are currently working to enable more accurate predictions of key outcomes like residual stress, distortion, and microstructure, leading to improved process optimization, reduced experimental costs, and accelerated development of new SSAM processes and materials.?

Looking for research collaborators in solid-state joining and additive manufacturing! Our team is working deep into the physics of these processes through numerical modeling and machine learning approaches. We have got some fascinating projects lined up that combine traditional manufacturing expertise with modern computational methods. We are especially keen to connect with researchers interested in FEA modeling, process optimization, and applying ML to manufacturing challenges. The work involves hands-on experience with advanced manufacturing systems and developing new computational frameworks. It is challenging but exciting work that could really contribute to sustainable manufacturing. If you're passionate about materials science and computational modeling, let's talk about potential collaborations. Connect us at: [email protected]