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.

拟议的项目旨在协作整合现代数据系统、实验、成像、机器学习和预测工程--用于添加剂制造(AM)和材料开发的物理模型。通过有重点的知识转移、密切的跨学科团队合作和学术-产业研究/资源的融合，该团队将共同建立一个系统的数据系统，涵盖不同规模的一系列材料的结构、性能、缺陷和变形，并将这些数据用于材料开发和AM过程优化。AM加工和表面处理对AM材料的表面完整性和功能特性(如耐腐蚀性)的影响有待系统地确定。该项目将开发实用的成像和处理算法，用于AM输入材料的分析、设计和联合质量控制，包括粉末生产。工程和关键物理模型将与机器学习相结合，用于预测组成和结构设计，以实现可印刷性、性能和性能之间的最佳协同。平衡印刷性能和结构性能的材料开发将侧重于AM中需要关键相控制的先进材料，包括双不锈钢、非晶态玻璃金属和镁。先进的数据和材料将作为AM未来研究和创新的枢轴平台，在整个产品开发生命周期内加快材料开发。通过有重点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技术在AM中的持续实际应用和开发，进一步提高特定应用产品在组成和结构上的设计自由度，并加快研究人员的发展，在欧盟和其他地区产生持久影响。