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arXiv 2409.14548physics.app-ph
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利用高精度金属模板基底研究激光粉末床熔融中熔道标度律的快速实验工作流

A rapid experimental workflow for studying melt track scaling in laser powder bed fusion using high-precision metal template substrates

Reimar Weissbach, Ryan W. Penny, A. John Hart

AI总结:

本文提出一种基于高精度蚀刻金属模板的单层LPBF实验工作流,在SS316中研究不同粉层厚度和熔化模式下的熔道标度律,并通过栅格扫描验证单道结果向完整构件转移的可行性。

摘要:

激光粉末床熔融(LPBF)中工艺参数的开发与鉴定通常涉及众多变量。在开发初期,无论是将已知参数转移到新设备,还是探索新材料,单道和单层实验都是筛选关键变量并研究参数标度行为的便捷手段。我们提出了一种使用蚀刻高精度金属模板基底进行单层LPBF实验的工作流,克服了LPBF系统中精密单层对位的挑战,并实现了高效加工和截面分析。模板采用化学蚀刻和机械加工制造,并通过光学轮廓测量和粉末层X射线透射成像进行表征。利用这些模板,在SS316中开展了单道参数研究,涵盖三种粉末层厚度,并跨越常见激光熔化模式(未熔合、热传导和匙孔模式)。通过自动化图像处理对熔道几何形貌进行分析,可应用标度律来界定工艺窗口,量化随粉末层厚度增加而添加的材料量。单道结果通过栅格扫描实验得到验证,表明单道结果具有向完整LPBF构件转移的潜力。

英文摘要:

Development and qualification of process parameters in laser powder bed fusion (LPBF) commonly involves many variables. At the outset of development, whether transferring known parameters to a new machine, or exploring a new material, single-track and single-layer experiments are a convenient means of down-selecting key variables and exploring parameter scaling behavior. We present an experimental workflow for single-layer LPBF experiments using etched high-precision metal template substrates, overcoming challenges with precision single-layer alignment in LPBF systems and enabling efficient processing and cross-sectional analysis. Templates are fabricated using chemical etching and machining, and are characterized using optical profilometry and X-ray transmission imaging of powder layers. Using the templates a single-track parameter study is performed in SS316 including three powder layer thicknesses, and spanning common laser melting modes (lack-of-fusion, conduction, and keyhole mode). Analysis of melt track geometries using automated image processing allows a scaling law to be applied to define the process window, quantifying the amount of material added with increasing powder layer thickness. Single-track results are verified with raster scanning experiments, showing the potential to transfer single-track results to full LPBF builds.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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