期刊
ADDITIVE MANUFACTURING
卷 34, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.addma.2020.101213
关键词
Laser powder bed fusion; Keyhole porosity; Machine learning; In-situ measurement; X-ray imaging
资金
- Laboratory Directed Research and Development (LDRD) from Argonne National Laboratory
- Office of Science, of the U.S. Department of Energy [DE-AC02-06CH11357]
- DOE Office of Science by Argonne National Laboratory [DE-AC02-06CH11357]
Additive manufacturing has the potential to revolutionize the production of metallic components as it yields near net shape parts with complex geometries and minimizes waste. At the present day, additively manufactured components face qualification and certification challenges due to the difficulty in controlling defects. This has driven a significant research effort aimed at better understanding and improving processing controls - yielding a plethora of in-situ measurements aimed at correlating defects with material quality metrics of interest. In this work, we develop machine-learning methods to learn correlations between thermal history and subsurface porosity for a variety of print conditions in laser powder bed fusion. Un-normalized surface temperatures (in the form of black-body radiances) are obtained using high-speed infrared imaging and porosity formation is observed in the sample cross-section through synchrotron x-ray imaging. To demonstrate the predictive power of these features, we present four statistical machine-learning models that correlate temperature histories to subsurface porosity formation in laser fused Ti-6Al-4V powder.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据