A Deep Learning Model for Estimating the Quality of Bimetallic Tracks Obtained by Laser Powder-Directed Energy Deposition.

During the fabrication of Inconel 718-AISI 316L bimetallic components via laser powder-directed energy deposition, understanding the relationships between processes, microstructures, and material properties is crucial to obtaining high-quality parts. Physical-chemical properties, cooling rates, and thermal expansion coefficients of each material may affect the microstructure of parts, generating segregations and cracks. This paper analyzes how the process parameters affect the dimensions, chemical composition, and microhardness of bimetallic tracks.