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.

拟议的项目旨在合作整合现代数据系统，实验，成像，机器学习和预测性工程 - 物理建模（AM）和材料开发。通过集中的知识转移，跨学科的团队合作以及学术工业研究/资源的融合，团队将共同建立一个系统的系统数据系统，以在不同规模的一系列材料中的AM中的结构，属性，缺陷和扭曲，并将数据用于材料开发和AM流程优化。 AM加工和表面处理对AM材料的表面完整性和功能特性（例如耐腐蚀性）的影响是系统地确定的。该项目将开发实用的成像和加工算法，以用于AM中输入材料的分析，设计和关节质量控制，包括粉末的生产。工程和关键的物理建模将与机器学习集成，以进行预测性组成和结构设计，以在可打印性，属性和性能之间进行最佳协同作用。材料开发平衡的可打印性和结构特性将集中在需要AM中关键相位控制的高级材料上，包括双板钢，无定形玻璃金属和MG。先进的数据和材料将成为AM未来研究和创新的枢纽平台，从而在完整的产品开发生命周期内加快了材料开发的速度。通过在多学科团队中重点的部门和国际知识交流和联合R＆I，该项目将有助于AM Industry 4.0技术和行业的行业4.0技术和开发的持续实践应用，进一步增强了应用特定产品的组成和结构的设计自由，并在EU和EU中加速了研究人员的开发，并加速了EU和EU的持久影响。