Rapid-Optimized Process Parameters of 1080 Carbon Steel Additively Manufactured via Laser Powder Bed Fusion on High-Throughput Mechanical Property Testing

基于高通量力学性能测试的激光粉末床熔融增材制造1080碳钢的快速优化工艺参数

阅读:1

Abstract

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。