Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys

Abstract

Additive manufacturing (AM), especially Laser Powder-Bed Fusion (L-PBF), provides alloys with unique properties, but faces printability challenges like porosity and cracks. To address these issues, a co-design strategy integrates chemistry and process indicators to efficiently screen the design space for defect-free combinations. Physics-based models and visualization tools explore the process space, and KGT models guide microstructural design. The approach combines experiments, databases, deep learning models, and Bayesian optimization to streamline AM alloy co-design. By merging computational tools and data-driven techniques with experiments, this integrated approach addresses AM alloy challenges and drives future advancements.