Data-Driven Performance-Oriented Rapid Process Optimization for 316 Stainless Steels Prepared by Laser Powder Bed Fusion

In this work, a rapid process optimization framework for laser powder bed fusion (LPBF) based on a high-throughput mechanical testing platform and data analytical methods was proposed and validated. This framework enables the efficient building of a process–properties database and analytical model, as well as the fine-tuning of customized mechanical properties. Unlike previous approaches that focused primarily on density as the main optimization target, this method directly aligns the mechanical properties by systematically varying the LPBF process parameters (e.g., laser power, scanning speed, etc.). Tensile specimens in the high densification range were prepared and tested using a high-throughput mechanical property test platform (HTP). Following this, an analytical model correlating tensile properties and process parameters was developed using response surface methodology (RSM). Based on this model recommendation, a specimen with a densification of 99.46% and a yield strength (YS) of 524.74 MPa was achieved, with only a 3.72% variation compared to the predicted value (526.08 MPa), confirming the model’s reliability. A comprehensive analysis of relative density, phase content and microstructure was conducted, comparing them with a specimen exhibiting lower properties. This study provides an effective method for the rapid evaluation and optimization of LPBF processing parameters for fine-tuning customized mechanical properties.