Artificial intelligence-driven approach to accelerate discovery of directed energy deposition of GRCop-42

GRCop-42 and GRCop-84 alloys are designed for high heat flux combustion chamber applications. GRCop-42 offers high thermal conductivity, high creep resistance, extended low-cycle fatigue lifetime, enhanced oxidation resistance, and high tensile strength. GRCop alloys with high thermal conductivity hinder efficient laser energy absorption, demanding higher laser powers or reduced scan speeds to maintain melt pool stability and avoid incomplete fusion. In this work, we employ an artificial intelligence (AI) driven framework to rapidly identify feasible process-parameter configurations for successful laser-directed energy deposition of GRCop-42 across a wide range of laser powers. To achieve this, we have developed an AI-guided discovery approach referred to as Bayesian Experimental design for Additive Manufacturing (BEAM). Using this BEAM process, we were able to print GRCop-42 between 500 and 700 Watt laser powers. Samples were characterized for their microstructure, phase analysis, and mechanical properties. Optical imaging of the samples shows that the distribution of the Cr2Nb varied depending on input energy density and related processing parameters. Compressive Yield strengths varied between 257 ± 31 and 332 ± 17 MPa, while the Vickers microhardness varied between 71 ± 5 HV0.2 and 142 ± 7 HV0.2. Our results highlight the benefit of AI-driven approaches in process optimization for difficult-to-manufacture materials using additive manufacturing.