Smart Residual Stresses Monitoring for Metal Additive Manufacturing – EM-ReSt

金属增材制造的智能残余应力监测 — EM-ReSt

• 批准号: 104038
• 金额: $ 156.54万
• 依托单位: ETHER NDE LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=104038
• 关键词: Smart; Residual; Stresses; Monitoring; Metal

"Metal Additive Manufacturing (AM) is an emerging technology for rapid prototype manufacturing, benefitting aerospace and medical devices, as the immediate manufacturing of high-value, complex structured components is usually necessary in these industries. Hence, the structural integrity of printed structures is extremely important and should meet the specifications and high standards of the above industries. In several metal AM techniques, e.g. selective laser melting (SLM), electron beam additive manufacturing (EBAM) and wire arc additive manufacturing (WAAM), residual stresses and micro-cracks that occur during the manufacturing procedure can result in irreversible damage and structural failure of the object after its manufacturing. Repetitive faults which occur during manufacturing due to incorrect estimation of appropriate operating conditions of the printer should be eliminated, as any waste is undesirable and costly for a company.The nature of some AM methods means that not all Non-Destructive Testing (NDT) techniques are effective in detecting residual stresses. Thermography, X-ray computed tomography (CT scan), or digital radiography are limited by the resolution of images (thermography), they are bulky and costly (up to £100k), are not suited to residual stress detection. Our solution, EM-ReSt, functions as an add-on to existing AM processes, comprising two sets of NDT techniques: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current Testing. A crucial (and novel) extension of the proposed system is the incorporation of big data collection from the sensors and analysis through machine learning (ML) for estimating the likelihood of the AM techniques to introduce anomalies into the printed structures before the beginning of the manufacturing. A digital system that will estimate the potential and deficiencies of any AM technique for given structures will be developed and utilised for the establishment of a preliminary set of AM standards. Hence, more robust and reliable components will be printed and used.EM-ReSt is fast (msecs/measurement and overall scanning time does not exceed a minute), reliable (90% PoD), non-destructive online monitoring of AM techniques, can achieve 15% reduction of faulty outputs with the use of 4 times more cost-effective monitoring system, has low profile sensing hardware with potential for EMAT and EC miniaturization. Our initial target markets are the global aerospace and automotive component manufacturing market. This project represents a clear technological innovation for the UK AM industry, and major growth opportunity for the SME supply chain consortium, which is forecast to generate revenues of £72.5M and 362 new jobs 5 years post-commercialisation."

“金属增材制造（AM）是一种用于快速原型制造的新兴技术，有利于航空航天和医疗设备，因为这些行业通常需要立即制造高价值，复杂的结构部件。因此，印刷结构的结构完整性是极其重要的，并且应该满足上述行业的规格和高标准。在几种金属增材制造技术中，例如选择性激光熔化（SLM）、电子束增材制造（EBAM）和电弧增材制造（WAAM），在制造过程中出现的残余应力和微裂纹可能导致物体在其制造之后的不可逆损坏和结构故障。由于对打印机的适当操作条件的不正确估计而在制造过程中发生的重复性故障应该被消除，因为任何浪费对公司来说都是不可取的和昂贵的。一些AM方法的性质意味着并非所有的无损检测（NDT）技术都能有效地检测残余应力。热成像、X射线计算机断层扫描（CT扫描）或数字射线照相术受到图像分辨率（热成像）的限制，它们体积庞大且昂贵（高达10万英镑），不适合残余应力检测。我们的解决方案EM-ReSt可作为现有AM工艺的附加组件，包括两套无损检测技术：电磁超声换能器（EMAT）和涡流检测。所提出的系统的一个关键（和新颖）扩展是通过机器学习（ML）结合来自传感器的大数据收集和分析，以估计AM技术在制造开始之前将异常引入打印结构的可能性。一个数字系统，将估计任何AM技术的潜力和不足之处，为给定的结构将被开发和用于建立一套初步的AM标准。EM-ReSt是快速（毫秒/测量和总扫描时间不超过一分钟）、可靠（90%PoD）、非破坏性的AM技术在线监测，可以通过使用4倍的成本效益监测系统实现15%的故障输出减少，具有低轮廓传感硬件，具有EMAT和EC小型化的潜力。我们最初的目标市场是全球航空航天和汽车零部件制造市场。该项目代表了英国AM行业的一项明显的技术创新，也是中小企业供应链联盟的主要增长机会，预计商业化后5年将产生7250万英镑的收入和362个新工作岗位。"