Kicking things off with real-time defect detection, this article is the first in a series delving into Artificial Intelligence (AI) and 3D printing, as the technology continues to be field-tested in ever more demanding applications to improve the quality of parts, automate the manufacturing process and even operate machines autonomously.
In 3D Printing Industry’s recent3D打印的未来：前沿技术专家观看调查, AI and Machine Learning (ML) were mentioned by a quarter of all those interviewed, making them the single most talked about technologies. Some respondents lauded them as ‘defining the frontier,’ while others saw them as a basis for “self-directing robots,” but elsewhere, firms likePrintpalare using them in the here and now.
PrintPal的首席执行官Peter Lebiedzinski说：“我们有一个在后台运行的异常检测系统，当打印机发生了很小的变化或存在异常时，该系统就会'感知'。”
“It is the equivalent of having an intern staring at the print constantly for the entire duration of the print without actually having to pay them $15 per hour,” he added. “This has very obvious benefits: saving time and material, and gives the user peace of mind by preventing damage or fire.”
Of course, anyone that’s ever returned to find build failure will know the frustration that comes with this, but it’s not just print faults that restrict 3D printing’s wider industrial adoption, it’s part consistency too. According to arecent studyby manufacturing service provider贾比尔, 37%的用户认为质量不足是一个问题that prevents scalability, while 39% blame technological limitations.
为了帮助用户提高他们的consiste一部分ncy and overcome frustrating failures, a number of firms like Printpal, as well as other researchers and institutes are now working on algorithms capable of alerting users to errors in real-time. To find out how these AI-driven software packages work, and where they could be applied in future, 3D Printing Industry has spoken to experts across the sector.
AI in unlocking 3D printing at scale
When it comes to manufacturing at an industrial-scale via technologies like Laser Powder Bed Fusion (LPBF), it’s vital to understand how every step in the process affects the quality of parts produced. In particular, the porosity of metal components can be an issue.
To get a firm grasp on what causes such porosity,Argonne National Research Lab’s诺亚·鲍尔森（Noah Paulson）和他的团队提出了method of using ML to predict defects。实际上，在将这些数据馈送到能够预测未来构建中地下孔隙率的机会之前，该团队已设法使用热成像来识别不同印刷零件的温度历史。
Paulson说：“原位ML孔隙度预测可以预测每个组件打印过程中缺陷形成的可能性。”“这提供了何时可能被损害的迹象，因此应保留以进行进一步的破坏性或无损性评估。由于这些技术更耗时且昂贵，因此可以在ML的帮助下最小化它们的使用。”
“Once trained, ML can predict the probability of sub-surface porosity for a new time-temperature history measurement.”
在其他地方，在美国另一家领先的研究机构，Oak Ridge National Lab（ORNL），科学家还正在开发一个数字框架，用于打印零件的认证和资格。为了实现这一目标，团队创建了“Peregrine,’ an algorithm designed to automatically detect anomalies in the texture features of each object layer, via digital imaging.
Thanks to its robust neural network architecture, it’s said that the software is able to reliably detect and identify the causes of multiple different classes of defects. Specifically, Peregrine finds out how certain morphological texture features correspond to anomalies, before using this data to create a model which can be deployed to inform future prints.
“By repeating the process over the entire height of the printed volume, it’s possible to create a 3D model of the component that can then be compared to the original design to produce a 3D map of all the pores,” explains Vincent Paquit, ORNL’s group leader for energy systems analytics. “Traditionally, this assessment is done using X-Ray CT imaging, a process requiring completing the manufacturing process.”
“Peregrine’s in-situ data analysis provides the same feedback, but in real-time, therefore allowing users to stop the process in case of the early detection of a catastrophic failure,” he added. “Although in-situ monitoring should not be seen as a replacement for X-Ray CT imaging, it does provide a complementary solution for statistical sampling.”
Realizing AI’s 3D printing potential
While AI 3D printing algorithms generally remain at a relatively early stage of development, researchers have started to experiment with early applications for them. Late last year, for instance, a joint team from宾夕法尼亚州立大学和US Air Force Research Lab（AFRL）利用ML生成独特的模型that identifies defects during builds, and deployed it to 3D print antenna parts.
“Our approach is a variation of in-line simulation, however ML surrogate prediction is very fast,” says Dr. Philip Buskohl, AFRL’s Research Mechanical Engineer on the project. “This requires paying the simulation costs upfront to create the training set, but opens the door for real-time prediction and subsequent defect repair.”
Two years ago,西部大学’sAliaksei Petsiuk also began working with known 3D printing innovator Joshua Pearce to develop anopen-source error-detection algorithm。Speaking to 3D Printing Industry, Pearce has now revealed that he continues to develop the technology with colleagues, as he says products likeSpaghetti Detectivetend to uncover big failures, but “miss more subtle issues.”
“Most recently, we have been getting stellar results by making synthetic images with Blender,” says Pearce. “We detect 3D printing anomalies by comparing images of printed layers from a stationary camera, with G-code-based reference images of an ideal process. This open-source method allows the program to notice critical errors in the early stages of their occurrence, and pause manufacturing.”
Identifying an AI-driven loophole?
However, while Buskohl and Pearce’s projects reflect the potential of AI technologies in advancing open-source 3D printing and defense manufacturing, research being carried out at纽约大学has uncovered some of its potential pitfalls.
利用机器学习，大学的工程师报告了玻璃和碳纤维的反向工程3D printed components. Though the team behind the project initially came up with their software to design complex parts and estimate their properties, they found that by ‘reversing the information flow,’ it’s possible to reconstruct a replica without the consent of designers.
According to Nikhil Gupta, an NYU Tandon Professor and one of the team behind the paper, this doesn’t necessarily mean that reverse engineering is currently happening, but he hopes its findings at least “alert the design community” to the possibility.
Gupta解释说：“目前似乎并未将ML方法用于逆向工程，因为复合材料的添加剂和用于复合材料设计的使用均处于新生阶段。”“但是，随着时间的推移，这些技术的成熟将增加反向工程的风险。”
AI’s future in real-time monitoring
Whether it be investigating the benefits or drawbacks of deploying AI in 3D printing, it’s clear that a significant amount of research is being poured into exploring its full potential. Although many of these algorithm-driven approaches being developed are still years from end-usage, the likes ofSigma Labs已经设法将它们的市场推向市场。
At present, the firm’s revenue remains relatively low, but it continues to foster close ties with government contractors. In fact, Sigma Lab’sPrintRite3Dreal-time monitoring software was even由洛克希德·马丁（Lockheed Martin）选择去年3月支持其太空部门。但是，尽管该平台目前由硬件和软件模块组成，但Sigma Labs现已揭幕计划启动仅软件产品, predicting that it could be adopted by as many as 10% of metal 3D printer users.
那么，这对3D印刷行业的AI意味着什么？根据Lebiedzinski的说法，该技术的最新进展表明，Printrite3D和他公司的PrintWatch等软件包将在该行业中解锁更大的自动化。PrintPal的首席执行官说，这可能对3D打印服务局特别有益，因为它使他们能够进行先发制的维护，以最大程度地减少机器停机时间，并确定基于提供最佳零件运输的订单。
“The end-goal of AI software is to run continuously without any human interaction,” concludes Lebiedzinski. “The software will automatically select print jobs, print them, sweep them off the bed into a reservoir and repeat the process. Machine Learning will be key to making this system work correctly and efficiently.”
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Featured image shows a diagram depicting what the AI model of Printpal’s PrintWatch software sees. Image via Printpal.
