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2025
Jun
27
Research Paper: Additive Manufacturing Metallurgy Guided Machine Learning Design of Versatile Alloys
This newly published work in Materials Today (Impact Factor: 22.0) by our Centre PI Dr. Sing Swee Leong and his team introduces a powerful data-driven approach to creating next-generation alloys for 3D printing. By combining insights from metallurgy with machine learning, the team developed a framework that can predict and optimise material performance specifically for additive manufacturing. The result: a new alloy with highly tunable strength and flexibility, opening doors to stronger, more reliable, and application-specific materials for advanced manufacturing.
This collaborative work is done by teams from National University of Singapore, Singapore Institute of Manufacturing Technology, Soochow University, Hunan University, City University of Hong Kong, Paul Scherrer Institute and University of Nebraska-Lincoln.
Read the review paper here
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