人工智能工程（AIE）软件公司Monolith AI和伦敦帝国学院have received a £500,000 grant from the UK’s innovation agency英国创新建立一种能够评估金属组件的生产性的新型AI工具。
该项目的目的是通过构建新版本的“可解释的AI”来彻底改变制造业中的计算机辅助工程（CAE），该版本可以向工程师提供明确的反馈，以了解为什么零件可能无法制造。
Monolith AI的首席执行官兼创始人Richard Ahlfeld博士说：“ CAE在推进组件制造业方面做得非常出色，但是在许多领域，物理模拟仍然无法捕捉组件的真正复杂性。”“大型工程公司在评估可制造性时收集了很多数据，我们的目标是使该数据发挥作用。
“This latest funding will allow us to explore this possibility and drive not only the automotive industry, but other sectors, forwards.”
AI-driven 3D printing
Over the past year, substantial progress has been made in the development of machine learning (ML) and AI-driven 3D printing technologies. In fact, AI was named alongside 3D printing, green hydrogen, and autonomous sensors in勒克斯研究’s最佳12 emerging technologies to watch在2021年，最近有预计，在明年，通过共享他们的增材制造业领导者共享他们的关键角色雷电竞充值2022 3D打印趋势.
AI software firmOqton, nowacquired by 3D printer manufacturer 3D Systems, is one of those leading the charge in this field. ItsML和AI驱动软件平台能够通过简化生产工作流程来自动化，加速和优化制造公司的吞吐量。
金属和复合3D打印机制造商标记has also embraced AI with the launch of itsAI-based Blacksmith softwarefor use with its X7 3D printer. The software deploys a patented scanning algorithm that measures the precision of parts as they are being printed and collects data to improve the quality of future jobs.
在过去的一年中，3D打印数据专家Senvol开始开发一个具有“其他功能”的ML软件解决美国国防部’s（国防部）生产需求，研究人员橡树岭国家实验室(ORNL) unveil theirPeregrine AI-driven real-time monitoring software他们声称这可能会将系统变成“自我校正机器”。
在其他地方，软件开发人员Autodesk已经成为一个founding partner of the nFrontier Emerging Technology Center, a dynamic lab environment seeking to整合“新兴技术”，其中AI是一个，在一个屋顶下。Fraunhofer ILThas also recently leveraged the benefits of AI to develop a 3D printed sensor system capable of intelligently maintaining train components, called感官.
评估零件的制造性
借助Innovate UK Grant，伦敦Monolith AI和帝国学院将与一群工业合作伙伴合作，在接下来的18个月中构建其新颖的AI工具。到项目结束时，合作伙伴希望已经开发了一种AI工具，该工具将能够评估金属组件是否可以制造，如果不是，则能够解释为什么是这种情况。
从本质上说,该项目将寻求建立的capabilities of current CAE simulations to help manufacturers not only predict whether a part can be successfully manufactured or not, but to assess the simulation results through ‘explainable AI.’
项目负责人乔尔·亨利（JoëlHenry）博士说：“知道一扇门是不够的。”“您需要了解原因，甚至更重要的是如何更改设计和操作条件以使其可制造。”
Once built, it is hoped the AI tool will be able to provide clear feedback to engineers on how it arrived at its conclusions in order to remove what the project partners call the ‘black box’ dilemma. The partners also hope to streamline the manufacturing process by leveraging AI learnings from what could be manufactured in the past to predict what would be best for new components and provide a new competitive advantage to high-volume manufacturers.
Using the AI tool, engineers could potentially build expert simulations based on repetitive tasks and historic data, and run complex manufacturability assessments in seconds rather than weeks. This would in turn free up engineering skills for other important tasks within the manufacturing workflow.
关于AI工具与3D打印的潜力，Ahlfeld说：“该技术在3D打印中的一种可能应用是根据CAD文件进行评估，是否可以通过打印设施打印此几何形状。想象一个在线原型网站，您可以在其中上传文件 - 该解决方案可以告诉您是否可以从以前的设计中学到几何形状。
“More, it could even highlight the parts of the geometry that could not be printed and explain why.”
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特色图片显示Monolith AI的首席执行官兼创始人Richard Ahlfeld博士。通过Monolith AI的照片。
