| Abstract Scope |
Additive manufacturing (AM) is distinguished by its near-net-shape fabrication capability, enabling single-step production of geometrically complex components. However, unlike conventional manufacturing processes, AM-fabricated parts generally lack post-process thermo-mechanical treatments. As a result, the performance of AM-built materials is predominantly governed by their composition and the thermal history inherent to AM. This underscores the necessity for developing materials dedicated to AM. To address this challenge, this talk introduces AM metallurgy-guided machine learning (ML) alloy design framework aimed at developing high-performance AM-specific alloys. Several case studies, including steels, aluminium alloys and titanium alloys will be elucidated. |