Contributions of porosity and laser parameter drift to inter-build variation of mechanical properties in additively manufactured 316 L stainless steel

SSRN Electronic Journal(2023)

引用 0|浏览12
暂无评分
摘要
The tensile mechanical properties of metals manufactured by laser powder bed fusion (L-PBF) are known to vary systematically between builds, posing challenges for the qualification and adoption of L-PBF. In this work, we systematically investigated two mechanisms that have previously been used to explain systematic differences in mechanical behavior of notionally identical L-PBF samples between builds (so called "inter-build variation"): porosity and laser parameter drift. Over 250 tension coupons, each with unique laser processing parameters, were built in 316 L stainless steel across three L-PBF builds, screened using high-resolution X-ray Computed Tomography to quantify internal porosity, and then mechanically tested. Notionally identical samples from one build showed statistically significant differences in porosity (0.045%, vs. 0.001% for the "best" build), ultimate tensile strength (614 vs. 588 MPa), and elongation (0.267 vs. 0.321 strain to failure), revealing meaningful levels of inter-build variation. Statistical and machine-learning guided interrogation of the relationships between laser processing conditions, porosity and mechanical response showed that the both laser parameter drift and sys-tematic differences in porosity can both adequately explain observations of inter-build variation, but their effects are nonlinear and are most relevant in different regimes of build quality. In the present case, laser parameter drift within high-density samples can result in material with improved strength and reduced ductility due to micro -structural refinement; on the other hand, porosity above-0.1% that is caused by process drift or other means contributes to rapid embrittlement, as in (Boyce et al., 2017).
更多
查看译文
关键词
Laser-powder bed fusion, Stainless steel, X-ray computed tomography, Mechanical properties, AM qualification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要