Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires ...Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.展开更多
基金support of NSF, United States, through Grant No. 1849085. BV, SS, and DS acknowledge Grant No. NSF-DGE-1545403 (NSF-NRT: Data-Enabled Discovery and Design of Energy Materials, D3EM)The authors would also like to acknowledge the NASA-ESI Program under Grant Number 80NSSC21K0223 and the ARPA-E ULTIMATE Program through Project DE-AR0001427RA acknowledges NSF-DMREF through Grant No. 2323611. High-throughput calculations were conducted partly at the Texas A&M High-Performance Research Computing (HPRC) Facility.
文摘Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.
