A team of researchers fromArgonne National Laboratory和Texas A&M Universityhave developed an innovative new approach to defect detection in 3D printed parts. Using real-time temperature data, together with machine learning algorithms, the scientists were able to make correlative links between thermal history and the formation of subsurface defects during the laser powder bed fusion process.
Aaron Greco, a co-author of the study, explains: “Ultimately you would be able to print something and collect temperature data at the source and you could see if there were some abnormalities, and then fix them or start over. That’s the big-picture goal.”
Porosities in 3D printed parts
As sophisticated as 3D printing is, even the higher-end industrial systems struggle with porosities – voids in the 3D printed part where metal powder has not fused sufficiently. These porosities often result in ‘weak spots’, making components prone to cracking and fractures.
孔隙率形成的原因有很多，包括不一致的粉末和激光优势不足。根据本文的主要作者诺亚·保尔森（Noah Paulson）的说法，阿贡（Argonne）的工作表明，零件的表面温度与内部的孔隙率形成之间存在明显的相关性。
Machine learning and powder bed fusion
To facilitate the research, the scientists made use of the high-powered X-rays atArgonne’s Advanced Photon Source(APS), a Department of Energy facility. The team designed and built an experimental PBF rig with in-situ infrared cameras, which would go on to 3D print parts made of Ti-64 powder. During printing, the camera was used to capture temperature data while the X-ray beam was used to view the printing process from the side, giving an indication as to whether or not porosities were being formed.
Paulson adds: “Having the top and side views at the same time is really powerful. With the side view, which is what is truly unique here with the APS setup, we could see that under certain processing conditions based on different time and temperature combinations porosity forms as the laser passes over.”
有趣的是，当将热历史与各自的孔隙率轮廓进行比较时，科学家发现低peak temperatures followed by gradual decreases were likely to be correlated with few porosities. On the other hand, high peak temperatures followed by dips and subsequent increases were likely to result in more porosities. Using their data sets, Paulson’s team then went on to build machine learning algorithms that could accurately predict porosity formations just based on the thermal histories recorded during the printing process.
The ability to identify where porosities are likely to form just from infrared imaging is a very powerful tool. It eliminates the need for costly individual part inspections, which are not always feasible when dealing with high production volumes. Paulson’s research team is hopeful that the work can be developed and improved with more data sets and a more sophisticated machine learning model in the coming months.
Further details of the study can be found in the paper titled ‘Correlations between thermal history and keyhole porosity in laser powder bed fusion’. It is co-authored by诺亚·鲍尔森（Noah H. Paulson）, Benjamin Gould, Sarah J. Wolff, Marius Stan, and Aaron C. Greco.
The predictive power of machine learning is really starting to be utilized in many aspects of additive manufacturing.Researchers from纽约大学最近使用的机器学习算法reverse engineer glass and carbon fiber 3D printed components。通过将3D打印零件的CT扫描喂入其型号，科学家们能够“窃取”用于制造零件的工具路径，同时保持了使他们的优势和耐用性的复杂性。
Elsewhere, at theSwinburne University of Technology, a researcher has used machine learning to give insight into thecompressive strength of 3D printed construction materials。为了开发一个过程，用于对不同的3D印刷地球聚合物样品进行分类，研究人员针对特定变量，并使用机器学习模型优化了3D打印材料的构成。
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Featured image shows X-ray imaging of the 3D printing process. Image via Argonne National Laboratory.
