Influence of Additive Manufacturing Route on the Mechanical Properties of SS316L: A Comparison Between LPBF and L-DED

Additive manufacturing (AM) of AISI 316 L stainless steel (SS316L) by Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (L-DED) is increasingly adopted to achieve complex geometries and process-specific microstructures, yet robust, quantitative guidelines linking process parameters to mechanical performance are still lacking. In this work, a systematic, cross-process assessment of SS316L fabricated by LPBF and L-DED is presented. Uniaxial tensile testing, Vickers microhardness measurements, full-field digital image correlation (DIC), and fractographic analysis are employed to characterize deformation behavior and failure mechanisms. The measured properties are benchmarked against a curated literature dataset so that component quality and representativeness with respect to state-of-the-art AM SS316L are ensured. To translate these results into design-relevant process–property relationships, a regression-based global sensitivity analysis is conducted on an integrated dataset that combines the present experimental results with published data over an extended processing window. Ordinary Least Squares (OLS) models and Standardized Regression Coefficients (SRCs) are used to rank the relative influence of laser power, scan speed, layer thickness, and hatch spacing on tensile strength and ductility. It is shown that both LPBF and L-DED produce non-equilibrium microstructures that enhance strength relative to conventionally processed SS316L, while statistically dominant parameters controlling mechanical performance are identified by the sensitivity analysis. In this way, actionable guidance is provided for process window optimization and property-driven design of AM SS316L components. • Comparative mechanical and microstructural assessment of LPBF and L-DED SS316L • LPBF provides higher strength and ductility than L-DED within the tested windows • DIC and fractography reveal distinct deformation and fracture mechanisms • Regression-based global sensitivity ranks process parameters for each AM route • Results deliver process–property guidelines for structural design of AM SS316L