Guoliang Huang

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.

It has become a trend to precisely control the additive manufacturing process parameters within the high-density process window to obtain high-performance metal parts. However, there are few reports on this topic currently, leaving this research without sufficient references. This study took 316L austenitic stainless steel as a case study. In total, 36 groups of specimens were manufactured by Laser powder bed melting (LPBF), and then, two highly dense specimens were selected to study the variation in their microstructure and properties. The densities of the selected specimens, S1 (VED = 81 J/mm³) and S2 (VED = 156.3 J/mm³), are 99.68% and 99.99%, respectively. The results indicated that, compared with the S1 specimen, the S2 specimen significantly decreased in terms of yield strength (YS), ultimate tensile strength (UTS), and elongation (EL), which are 7.28%, 6.34%, and 19.15%, respectively. The differences in mechanical properties were primarily attributed to differences in their microstructures. Further, compared with the S1 specimen, the fitted ellipse aspect ratio and average grain size of the S2 specimen increased by 79.88% and 53.45%, respectively, and the kernel average misorientation (KAM) value and geometric necessary dislocation (GND) density increased by 36.00% and 58.43%, respectively. Furthermore, the S1 specimen exhibited a strong texture in the //Z direction, whereas no obvious texture was observed in the S2 specimen. Obviously, the reason why precise regulation within the dense parameter range can achieve better performance is that the microstructure and mechanical properties of the specimens prepared within the dense range are different. More importantly, this study provides a feasible framework for optimizing alloys with broad and dense parameter ranges, demonstrating the potential to achieve high-performance components through precise parameter control. Furthermore, the results reveal that even within a wide range of high-density forming parameters, significant variations in microstructure and mechanical properties can arise depending on the selected parameter combinations. These findings underscore the critical importance of meticulous process parameter optimization and microstructural regulation in tailoring material properties.

To ensure the sustainability of alloy-based strategies, both compositional design and processing routes must be simplified. Metal additive manufacturing (AM), with its exceptionally rapid, non-equilibrium solidification, offers a unique platform to produce tailored microstructures in simple alloys that deliver superior mechanical properties. In this study, we employ laser powder bed fusion (LPBF) to fabricate 1080 plain carbon steel, a binary alloy comprising only iron and carbon. Deviating from conventional process optimization focusing primarily on density, we optimize LPBF parameters for mechanical performance. We systematically varied key parameters (laser power and scan speed) to produce batches of tensile specimens, which were then evaluated on a high-throughput mechanical testing platform (HTP). Using response surface methodology (RSM), we developed predictive models correlating these parameters with yield strength (YS) and elongation. The RSM models identified optimal and suboptimal parameter sets. Specimens printed under the predicted optimal conditions achieved YS of 1543.5 MPa and elongation of 7.58%, closely matching RSM predictions (1595.3 MPa and 8.32%) with deviations of −3.25% and −8.89% for YS and elongation, respectively, thus validating model accuracy. Comprehensive microstructural characterization, including metallographic analysis and fracture surface examination, revealed the microstructural origins of performance differences and the underlying strengthening mechanisms. This methodology enables rapid evaluation and optimization of LPBF parameters for 1080 carbon steel and can be generalized as an efficient framework for robust LPBF process development.