Low-Power Direct Energy Deposition of 316L Stainless Steel: Process Parameters, Volumetric Characterization, and Predictive Modeling

Low-power direct energy deposition (DED) can be a viable strategy to manufacture 316L stainless steel parts with low porosity and distortion, enabled by a novel predictive modeling and optimization framework. This study assessed the methodology of a CNC-integrated DED system with four powder nozzles and a custom-made G-Code postprocessor (DEDRA).Over 150 experiments were conducted to systematically adjust laser power, traversing speed, powder feed rate, hatch spacing, and interlayer height. Furthermore, ten mathematical models were applied to predict geometry (internal and external) and porosity with an error below 4% across five parameter sets. These models can calculate single-track welds and their superpositions during layer-by-layer deposition, thereby reducing thermal distortion and residual stress. The optimized builds, characterized by their grain size, chemical composition, and microstructure, achieved porosity levels between 3 and 4.85% and defect rates of nearly zero (0–69 ppm). Dimensional accuracy was preserved without excessive layer growth, while microhardness equaled or surpassed that of forged 316L. Microstructural characterization revealed refined grain morphology, improved homogeneity, and the reduction of defect formation under optimized conditions. This study tackles the limitations of prior high-power advances and trial-and-error approaches. The research provides a modeling framework with a foundation for reliable, repeatable, and energy-efficient additive manufacturing of stainless steel. It sets the groundwork for future real-time monitoring and enhanced process control.