Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufac

先进实验、成像和计算的集成，用于增材制造中粉末和材料的协同结构-性能设计

• 批准号: EP/Y036778/1
• 负责人: Yonghuai Liu
• 金额: $ 13.9万
• 依托单位: Edge Hill University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY036778%2F1
• 关键词: Integration; Advanced; Experiments; Imaging; Computation

The proposed project aims to collaboratively integrate modern data system, experiments, imaging, machine learning and predictive engineering-physical modelling for additive manufacturing (AM) and materials developments. Through focused knowledge transfer, close interdisciplinary teamwork and fusion of the academic-industrial research/resource, the team will jointly establish a systematic data system of the structure, properties, defects and distortions in AM of a range of materials at different scales and use the data for materials development and AM process optimisation. The effect of AM processing and surface treatments on the surface integrity and functional properties (e.g. corrosion resistance) of AM materials is to be systematically established. The project will develop practical imaging and processing algorithms for the analysis, design, and joint quality control for the input materials in AM, including powder production. Engineering and key physical modelling is to be integrated with machine learning for predictive composition and structure design for optimum synergy between printability, properties and performances. Materials development balancing printability and structure properties will be focused on advanced materials requiring critical phase control in AM, including duplexstainless steels, amorphous glass metals and Mg. The advanced data and materials will serve as a pivoting platform for future research and innovation in AM, speeding up material development within the full product development life cycle. Through focused intersectoral and international knowledge exchange and joint R&I within a multidisciplinary team, the project will contribute to the continuous practical applications of Industry 4.0 technologies and development for industry5.0 in AM, further enhancing the design freedom in composition and structure for application-specific products, and accelerating the researcher development with lasting impact in the EU and beyond.

拟议项目旨在协作集成现代数据系统、实验、成像、机器学习和预测工程物理建模，以实现增材制造 (AM) 和材料开发。通过集中的知识转移、紧密的跨学科团队合作以及学术与工业研究/资源的融合，团队将共同建立不同尺度的一系列材料的增材制造结构、性能、缺陷和变形的系统数据系统，并利用用于材料开发和增材制造工艺优化的数据。需系统地确定增材制造加工和表面处理对增材制造材料的表面完整性和功能特性（例如耐腐蚀性）的影响。该项目将开发实用的成像和处理算法，用于增材制造中输入材料（包括粉末生产）的分析、设计和联合质量控制。工程和关键物理建模将与机器学习相结合，以进行预测成分和结构设计，以实现适印性、特性和性能之间的最佳协同作用。平衡可印刷性和结构性能的材料开发将侧重于增材制造中需要关键相控制的先进材料，包括双相不锈钢、非晶玻璃金属和镁。先进的数据和材料将作为增材制造未来研究和创新的枢纽平台，加快整个产品开发生命周期内的材料开发。通过集中的跨部门和国际知识交流以及多学科团队的联合研发，该项目将有助于工业4.0技术的持续实际应用和工业5.0在增材制造领域的发展，进一步增强特定应用的组成和结构的设计自由度产品，并加速研究人员的发展，对欧盟及其他地区产生持久影响。