Optimizing Binder Jet Additive Manufacturing Parameters

Abstract A physics-informed multi-agent framework demonstrates how physics-constrained AI can accelerate industrial adoption of binder jet additive manufacturing (BJAM) by delivering first-time right process parameters. By decomposing optimization into specialized agents that handle physics enforcement, materials classification, and uncertainty quantification, the framework captures cross-material trends while ensuring physical realism, thereby transforming BJAM development from empirical iteration into predictive engineering.