Oak Ridge National Laboratory(ORNL)researchers have developed Artificial Intelligence (AI) software capable of monitoring the metal 3D printing process in real-time.
该算法被昵称为百富林，以具有成本效益的表征设备替代品评估生产过程中零件的质量。该程序是ORNL更广泛的“数字线程”的一部分，该计划通过制造过程的每个步骤仔细跟踪和分析数据。在未来的工厂中，ORNL团队认为它们的算法可以用作自我校正机器的质量控制方法。
Automating metal 3D printing
During powder-based metal 3D printing, a number of issues can arise which negatively impact upon the features of the end-use printed part. The uneven distribution of powder or binding agent, spatters, insufficient heat, and porosities can lead to defects on the surface of each layer. Many of these printing issues go undetected using conventional monitoring techniques, leading to suboptimal parts or the component being scrapped altogether.
Although some production anomalies happen very quickly and can be difficult to prevent, others are more predictable and large enough to make layer-wise detection possible.
“One of the fundamental challenges for additive manufacturing is that you’re caring about things that occur on length-scales of tens of microns and happening in microseconds,” said Luke Scime, Principal Investigator for Peregrine. “Because a flaw can form at any one of those points at any one of those times, it becomes a challenge to understand the process and to qualify a part.”
According to the ORNL team, prior attempts to monitor 3D printing errors in real time have not been automated enough to allow their widespread adoption in factory settings. In terms of analyzing the data collected during the production process, previous approaches have also focused on comparing the finished part to its 3D model. The benefit of a comparative approach is that it’s simple, but it’s also time limiting, and only identifies one ‘generic flaw’ in a printed object.
ORNL的花生软件
In order to overcome the limitations observed in earlier research, the ORNL team developed a novel Convolutional Neural Network (CNN) architecture. The computer vision technique uses a custom-designed algorithm to carefully examine the pixel values of images taken during the printing process.
实际上，当在打印过程中检测到异常时，该软件能够提醒机器操作员需要进行调整。二重晶在人脑上建模，也能够分析和共享多个3D打印机的表面可见缺陷的图像。结果，使用ORNL算法，一个系统能够从另一个系统遇到的打印错误中学习。
“捕获信息为每个部分创建了数字'克隆'，从原材料到操作组件提供了一系列数据，” Vincent Paquit解释说，他领导了Ornl成像，信号和机器学习组的一部分。“然后，我们使用该数据来限定零件，并通过多个几何形状和多种材料告知未来的构建。”
Testing ORNL’s deep learning method
In order to test Peregrine’s deep learning algorithm, the researchers provided it with a data set for evaluation. Objects were 3D printed across eight different Laser Powder Bed Fusion (LPBF) and binder jetting machines. Two 8-bit images were captured per camera, for each layer, and on every system, with one immediately following powder fusion or binder deposition, and the other after powder spreading.
The performance of the algorithm was then measured based on the amount of inter-machine transfer learning that took place. Evaluating the data shared between aGE AdditiveConceptLaser M2 and anExone Innovent该团队发现，16％的测试损失了10,000批测试批次。因此，尽管该算法证明了其在系统之间共享信息的能力，但有关标记为异常的大量歧义仍然存在。
For instance, parts printed using anArcam Q10增材制造系统报告的真实孔隙率比为78.4％，但是如果两个像素的空间中的孔子比率均为折扣，则上涨至89.5％。还发现该算法的性能很大程度上取决于其输入数据。如果在捕获的图像中尚不清楚打印零件中的异常，则该软件不太可能标记。结果，在测试过程中完善照明和成像配置可能会改善平毛的表现。
Overall, the algorithm achieved segmentation times of 0.5-2.4 seconds per layer, making it rapid enough for applications in serial manufacturing. The software also demonstrated the ability to pass significant amounts of data from machine to machine, across different 3D printing technologies. In future research, the team concluded that they will explore different imaging wavelengths, lighting conditions, and spatially mapped sensor modalities.
Nuclear applications of ORNL’s algorithm
Over the last six months, Peregrine has been tested on hundreds of builds at ORNL, including as part of its 3D printedTransformational Challenge Reactor（TCR）程序。ORNL的TCR项目旨在在2023年之前使用一组集成的传感器和控件来捕获数据。
ORNL研究团队已经部署了新开发的算法来优化该过程，从而降低了与构建反应堆核心相关的交货时间和成本。
“Establishing correlations between the signatures collected during manufacturing and performance during operation will be the most data-rich and informed process for qualifying critical nuclear reactor components,” said Kurt Terrani, TCR program director. “The fact that it may be accomplished during manufacturing to eliminate the long and costly conventional qualification process is the other obvious benefit.”
完全自动的3D打印
除了潜在的核应用,the Peregrine software could also provide automated quality control within self-sustaining 3D printing factories. A number of companies have developed their own ‘factory of the future’ projects in recent years, with the aim of fully-automating the manufacturing process.
EOS,戴姆勒,和Premium AEROTEC成功驾驶他们的NextGenam项目in a German plant. The self-sustaining factory’s software has a digital twin feature, which makes it possible to “copy and paste” the same system of machines in order to increase capacity. All stages of the process were automated, including powder handling, print bed removal, post-processing, and quality inspection.
汽车制造商宝马is developing its own self-regulating processes in the form of itsIDAM和多线项目。作为其IDAM冒险的一部分，宝马旨在将至少50,000个组件的生产数字化，每年至少10,000多个个人和备件。在其多线计划中，该公司正在与15个合作伙伴合作开发以数字链接的过程步骤，并采用一致的质量保证方法。
The researchers’ findings are detailed in their paper titled “Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation，”Additive Manufacturingjournal. The report was co-authored by Luke Scimea, Derek Siddel, SethBaird, and Vincent Paquita.
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Featured image shows researcher Chase Joslin using the Peregrine software to monitor and analyze a component being 3D printed. at ORNL. Photo via Luke Scime/ORNL.
