Insiders and analysts predict the 3D printing trends to watch in our latest series of articles focused on thefuture of 3D printing。
来自德国和加拿大的组织集合建立了一个新的财团，以使用3D打印和人工智能自动修复零件的过程。
该项目称为自适应激光添加剂制造（AI-SLAM）的工艺感测的人工智能增强，旨在开发基于AI的高级软件以自动运行定向能量沉积（DED）3D打印机。雷电竞充值雷电竞app下载该软件与Fraunhofer的LMD技术（一种DED）结合使用，将算法管理打印过程，以更有效地修复受损部件的不规则表面 - 无需人类输入。
On the German side, the consortium includes theFraunhofer Institute for Laser Technology（ILT）和软件开发人员BCT。In Canada, the work will be overseen by theNational Research Council of Canada(NRC),麦吉尔大学will coordinate the research, and machine learning firmBraintoywill help program the AI models. Alberta-based manufacturing service bureau阿波罗机器和焊接will also be providing additional 3D printing services to aid the research.
DED 3D printing for part repair
According to the AI-SLAM consortium, manufacturing firms that serve the mining and petroleum sectors receive a large number of orders for repairing parts that have been worn down due to extensive use. Apollo, for example, uses several tons of metal every year to repair worn parts. This includes components such as rock crusher teeth, which would otherwise need to be replaced due to changes in shape over time.
Thanks to DED’s ability to 3D print on existing parts and surfaces, the technology is often used for part repair and MRO applications. Users can simply print new layers on worn parts to reconstruct the original shape of the surface.
不幸的是，经历过重磨损的零件往往具有不平衡的表面。这要求在3D打印过程中使用不同的层厚度，因此机器操作员需要每隔几层调整过程参数。由于这种工作流程非常乏味，该财团旨在通过使用AI自动化它。
AI and automated process control
The AI-SLAM software will work by automatically recording the geometry of the worn part during the printing process, calculating the deviation from the desired contour, and readjusting the process parameters to achieve the geometry of the original part. The process control software will have full access to parameters such as the feed rate, which it will optimize on a layer-by-layer basis using machine learning algorithms.
To train the software in the first place, the consortium will feed it with a large dataset to teach it how to iteratively improve the 3D printing process based on an ever-changing part geometry. So far, the project partners have already held regular video meetings and jointly developed several documents online. They’ve also taken virtual laboratory tours of each other’s facilities to become familiar with the workspaces on both continents.
为了启用流程数据和机器学习模型的实施，Braintoy已为所有项目合作伙伴激活了其内部网络平台MLOS（机器学习操作系统）。该联合项目定于2024年3月完成，并由德国联邦教育和研究部以及加拿大的NRC资助。
Machine learning has a number of applications in additive manufacturing, beyond just automating process control.Markforged, a manufacturer of metal and composite 3D printers, has previously launched its基于人工智能的铁匠软件。Leveraging the X7 3D printer’s existing laser micrometer and a patented scanning algorithm, Blacksmith accurately measures the dimensional precision of parts as they are being printed. This scan data is then cross-referenced against the intended design files, giving manufacturers the ability to assess just how close the printed part is to the original STL.
在其他地方，3D打印软件提供商重力拉系统has previously launched amachine learning-based workflow optimization system用于工业3D打印。该软件被称为Synoptik，旨在服务局，为航空航天，医疗和汽车等各种行业提供服务。通过为3D打印工作流程的每个步骤提供优化操作，从预处理到后处理和质量检查，Synoptik旨在填补所有的利基市场。
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图片显示了一个圣one crusher tooth being 3D printed using Fraunhofer’s LMD process. Photo via Apollo Machine and Welding.
