Optimization of in-situ laser remelting parameters for enhancing mechanical properties of parts produced by laser powder bed fusion process

•Poor surface roughness of the intermediate layers during the Laser Powder Bed Fusion (L-PBF) process has a detrimental impact on the mechanical properties of the final component.•The present study proposes an integrated computational framework comprising a surface generation model and a Computational Fluid Dynamics (CFD) simulation model is employed to simulate the surface roughness of the L-PBF processed surface morphology following the application of Laser Re-melting (LR).•The developed computational model is combined with surrogate model to predict optimal Laser Re-melting (LR) conditions for hybrid L-PBF+LR method.•The feasibility of the proposed hybrid L-PBF+LR method is experimentally demonstrated using SS316L powder as the feedstock material. Tensile test specimens fabricated using the optimal processing conditions show an overall tensile strength of 550 ± 7.8 MPa and an overall yield strength of 420 ± 5.9 MPa. The Laser Powder Bed Fusion (L-PBF) process employs a controlled laser beam to melt specific regions of a metal powder layer in a layer-by-layer manner to fabricate parts with complex geometries. Owing to its stochastic nature, defects such as distortion, porosity, and poor surface roughness often occur during the L-PBF process. Poor surface roughness of the intermediate layers during the build process has a detrimental impact on the mechanical properties of the final component. Furthermore, poor surface roughness may also cause the mechanical properties of the components fabricated at different locations on the build plate to be inconsistent. Accordingly, the present study uses a Laser Remelting (LR) technique to improve the surface roughness of the intermediate layers during the L-PBF build process. An integrated computational framework comprising a surface generation model and a Computational Fluid Dynamics (CFD) simulation model is employed to simulate the surface roughness of the L-PBF processed surface morphology following the application of LR. The framework is verified using experimental data and then combined with a surrogate model to predict the melt pool depth and surface roughness for any combination of the laser power, scanning speed, and initial surface roughness within the feasible design space. The LR processing conditions that minimize the surface roughness and melt pool depth, and hence improve the density of the final component, are determined by screening the predictions of the surrogate model using two criteria: (1) a pr