AI-guided experiments identify low-power DED parameters for NASA’s GRCop-42 alloy

Researchers at Washington State University and the University of Minnesota have developed an AI-guided experimental design method that identified feasible Directed Energy Deposition (DED) parameters for GRCop-42 at laser powers as low as 500 W.

Called Bayesian Experimental design for Additive Manufacturing (BEAM), the method uses previous print results to select promising parameter combinations for testing. Applied to NASA-developed GRCop-42, it identified successful configurations from 950 W down to 500 W within ten trials at each power level.

Using AI to search for feasible DED conditions

Metal AM process development can involve millions of parameter combinations, making trial-and-error testing costly and slow. BEAM narrows this search using an adaptive loop that learns from previous