Configuring additive manufacturing (AM) processes for metal alloys is challenging because printed output quality depends on complex interactions among process parameters such as laser power, scan speed, and feed rate. Conventional trial-and-error approaches are inefficient because experiments are costly and the process parameter space is extremely large. More broadly, this setting reflects a growing class of scientific discovery problems in which experiments are expensive and experimental resources must be allocated intelligently. This paper presents a Bayesian Experimental design for AM (BEAM) methodology that combines principles of AI-driven adaptive experimental design with domain knowledge to accelerate discovery of feasible process configurations. BEAM treats process development as a closed-loop learning problem: a probabilistic surrogate model learns from prior experiments and iteratively recommends promising configurations for laboratory validation. This creates a human-AI collaborative workflow in which domain experts define physical constraints, AI prioritizes experiments, and laboratory feedback improves future recommendations. We deploy BEAM on a directed energy deposition (DED) process to print GRCop-42, a NASA-developed copper alloy that is difficult to process using conventional infrared laser systems because of its low laser absorptivity and high thermal conductivity. Within three months, BEAM discovered multiple defect-free process configurations across laser power levels from 950 to 500W, dramatically reducing time and resource expenditure compared to several months of unsuccessful manual experimentation. By enabling high-quality GRCop-42 fabrication on widely available lower-power infrared laser platforms for the first time, this work demonstrates how AI-guided experimental design can accelerate scientific and engineering discovery in resource-constrained settings.