Data-driven, Reliable, and Effective Additive Manufacturing using multi-BEAM technologies (DREAM BEAM)

使用多光束技术 (DREAM BEAM) 进行数据驱动、可靠且有效的增材制造

• 批准号: EP/W037483/1
• 负责人: Chu Lun Alex Leung
• 金额: $ 50.86万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW037483%2F1
• 关键词: Data; driven; Reliable; Effective; Additive

Laser powder bed fusion (LPBF) additive manufacturing (AM) transforms digital designs into functional products by joining materials together, layer upon layer. It offers flexible, sustainable manufacturability and short product development time to produce high-value components with complex geometries for business across the globe, including aerospace, automotive, and biomedical sectors. The global market for AM is expected to grow from $6b (2016) to $26b (2022), resulting in major initiatives launched across the globe to grow AM technologies, including "UK Industrial strategy", "Fraunhofer Additive Manufacturing Alliance", "Made in China 2025", and "America Makes". Despite the key advantages of AM, industries are facing technical challenges to use AM technology for safety-critical products, e.g. propellers and turbine blades, etc. These products may exhibit poor mechanical performance due to the presence of processing defects. To produce high-performance AM products, the stakeholders must understand the process and defect dynamics during AM, however, they are difficult to characterise due to the fast, complex laser-matter and multi-phase (solid-liquid-gas-plasma) interactions which occur in milliseconds. This project involves UCL and world-leading industrial partners in AM (Renishaw plc.), laser technologies (STFC - Central laser facility), machine learning (STFC - Scientific Machine-learning group), ultra-fast imaging (European Synchrotron Radiation Facility) and process simulations (European Space Agency) to co-develop engineering solutions to understand, evaluate, and control the process-structure-property-performance relationships in AM. This project is expected to collect a wide range of digital data that can be used to develop a data-driven, reliable and efficient AM process. Firstly, a unique chemical imaging tool will be developed and deployed to monitor and evaluate the metal vapourisation process during LPBF with a temporal resolution of 200 kHz. These results will be cross-validated by flagship ultra-fast X-ray imaging experiments which enable users to see inside the melt pool and defect dynamics during LPBF at micron resolution and a time resolution of up to 1 MHz. Correlative chemical and X-ray imaging of AM will be a game-changer characterisation technique to study the dynamic behaviour and multiphase interaction in AM. It will bring new understanding by which defects are introduced during AM and suggest ways to improve the overall process. Secondly, we will make advancement of novel beam shaping technologies to control the heat input to the fusion process, minimising metal vapourisation and defect formation during LPBF. The performance of the beam-shaping technologies will be assessed and verified by correlative imaging. Thirdly, all the digital data collected through this project will be used to build, train and deploy machine learning (ML) model(s) for process control, i.e. ML-guided process control. They will also be used to verify, validate, and advance an open-source high fidelity process simulation model that analyses multi-phase and multi-physics interactions in AM, which can be extended to other advanced manufacturing processes. Besides the development of new technologies, this project will also provide opportunities for early-career researchers to disseminate their research to the public, industries, and scientific communities, promote knowledge exchange and technology transfer activities.

激光粉末床熔融 (LPBF) 增材制造 (AM) 通过将材料逐层连接在一起，将数字设计转化为功能性产品。它提供灵活、可持续的可制造性和较短的产品开发时间，可为全球业务（包括航空航天、汽车和生物医学领域）生产具有复杂几何形状的高价值组件。全球增材制造市场预计将从 60 亿美元（2016 年）增长到 260 亿美元（2022 年），全球各地纷纷推出发展增材制造技术的重大举措，包括“英国工业战略”、“弗劳恩霍夫增材制造联盟”、“中国制造 2025”和“美国制造”。尽管增材制造具有关键优势，但各行业在将增材制造技术用于安全关键产品时面临着技术挑战，例如：螺旋桨和涡轮叶片等。这些产品可能由于存在加工缺陷而表现出较差的机械性能。为了生产高性能增材制造产品，利益相关者必须了解增材制造过程中的工艺和缺陷动态，然而，由于快速、复杂的激光-物质和多相（固-液-气-等离子体）相互作用在毫秒内发生，因此很难表征它们。该项目涉及伦敦大学学院和增材制造（雷尼绍公司）、激光技术（STFC - 中央激光设施）、机器学习（STFC - 科学机器学习小组）、超快成像（欧洲同步辐射设施）和过程模拟（欧洲航天局）领域的世界领先工业合作伙伴，共同开发工程解决方案，以理解、评估和控制过程-结构-性能-性能 AM 中的关系。该项目预计将收集广泛的数字数据，可用于开发数据驱动、可靠且高效的增材制造流程。首先，将开发和部署独特的化学成像工具，以 200 kHz 的时间分辨率监测和评估 LPBF 期间的金属汽化过程。这些结果将通过旗舰超快 X 射线成像实验进行交叉验证，使用户能够在 LPBF 期间以微米分辨率和高达 1 MHz 的时间分辨率查看熔池内部和缺陷动态。 AM 的相关化学和 X 射线成像将成为研究 AM 动态行为和多相相互作用的一种颠覆性表征技术。它将带来新的认识，哪些缺陷是在增材制造过程中引入的，并提出改进整个流程的方法。其次，我们将推进新型光束整形技术来控制熔化过程的热量输入，最大限度地减少 LPBF 期间的金属蒸发和缺陷形成。光束整形技术的性能将通过相关成像进行评估和验证。第三，通过该项目收集的所有数字数据将用于构建、训练和部署用于过程控制的机器学习（ML）模型，即机器学习引导的过程控制。它们还将用于验证、验证和推进开源高保真工艺仿真模型，该模型可分析增材制造中的多相和多物理场相互作用，并可扩展到其他先进制造工艺。除了开发新技术外，该项目还将为早期职业研究人员提供机会，向公众、行业和科学界传播他们的研究成果，促进知识交流和技术转让活动。

项目成果

Additively manufactured high-energy-absorption metamaterials with artificially engineered distribution of bio-inspired hierarchical microstructures

增材制造的高能量吸收超材料，具有人工设计的仿生分层微结构分布

• DOI: 10.1016/j.compositesb.2022.110345
• 发表时间: 2022
• 期刊: Engineering
• 影响因子: 12.8
• 作者: Gao Z

A high-fidelity comprehensive framework for the additive manufacturing printability assessment

• DOI: 10.1016/j.jmapro.2023.09.041
• 发表时间: 2023-11
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: L. Guo;Hanjie Liu;Hongze Wang;Qianglong Wei;Jiahui Zhang;Yingyan Chen;Chu Lun Alex Leung;Qing Lian;Yi Wu;Yu Zou;Haowei Wang

Synchrotron validation of inline coherent imaging for tracking laser keyhole depth

用于跟踪激光小孔深度的内联相干成像的同步加速器验证

• DOI: 10.1016/j.addma.2023.103798
• 发表时间: 2023
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: Fleming T

In situ characterisation of surface roughness and its amplification during multilayer single-track laser powder bed fusion additive manufacturing

• DOI: 10.1016/j.addma.2023.103809
• 发表时间: 2023-10
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: Alisha Bhatt;Yuze Huang;C. L. Leung;Gowtham Soundarapandiyan;S. Marussi;Saurabh Shah;Robert C. Atwood-Robe

In situ Correlative Observation of Humping-Induced Cracking in Directed Energy Deposition of Nickel-Based Superalloys

• DOI: 10.1016/j.addma.2023.103579
• 发表时间: 2023-04
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: Tristan G. Fleming;David Tien Rees;S. Marussi;T. Connolley;R. Atwood;M. Jones;J. Fraser;Chu Lun;Alex Leung;P. D. Lee