FMSG: Cyber: Federated Deep Learning for Future Ubiquitous Distributed Additive Manufacturing

FMSG：网络：面向未来无处不在的分布式增材制造的联合深度学习

• 批准号: 2134689
• 负责人: Jia Liu
• 金额: $ 49.88万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134689&HistoricalAwards=false
• 关键词: FMSG; Cyber; Federated; Deep; Learning; FMSG; Cyber; Federated; Deep; Learning

Distributed additive manufacturing has promising potential to connect and coordinate individual manufacturers for efficient, on-demand production. It can leverage the freeform fabrication of numerous additive manufacturers to form a flexible and robust supply chain and achieve reconfigurable mass customization in the future. However, product quality, consistency and privacy concerns among those distributed manufacturers pose a grand challenge to fully unleashing the potential of distributed additive manufacturing. This Future Manufacturing Seed Grant (FMSG) CyberManufacturing project will support fundamental research to provide needed knowledge for developing a unified algorithmic and training framework. The new framework, named FEDMDL, will lay a solid foundation to enable consistent and reliable production in a privacy-preserving, insight-sharing manufacturing network. This will further promote the adoption of additively manufactured parts in various industries, such as aerospace, automobile, healthcare, and will boost the participation of small-and-medium-sized manufacturers in the national supply chain. Therefore, results from this research will benefit the competitive advantages of US manufacturing and economy. This research provides manufacturing companies with the synergy of novel machine learning and federated computing techniques. The multi-disciplinary approach will help broaden the participation of underrepresented groups in research and positively impact engineering education. The unified algorithmic and training framework, FEDMDL, will chart a new theoretical path to enabling reliable production, consistent quality, and privacy-preserving data sharing in distributed additive manufacturing. FEDMDL will synthesize the fundamental physics of additive manufacturing processes into deep learning algorithms and train the new models on a federated learning cyberinfrastructure. In this seed grant, FEDMDL will be prototyped with fatigue performance assessment of additively manufactured metals in a distributed manufacturing network. The research team will: (1) conduct fatigue testing and defect characterization to understand material-defect-geometry-loading-fatigue relationships; (2) develop fracture-mechanics-centric deep learning models to approximate multi-physics multiscale processes and predict the fatigue performance of complex geometries under multiaxial loading; (3) design a cross-silo, additive-manufacturing-aware federated learning cyberinfrastructure to train the deep learning models with collective insights from the sparse, siloed datasets across manufacturers; and (4) evaluate the framework by deploying it in a real-world distributed additive manufacturing network. This work will result in an experimentally validated, generalizable algorithmic and training framework to catalyze research and applications in quality modeling, qualification, and control for future distributed additive manufacturing with collective intelligence.This project is jointly funded by the Division of Civil. Mechanical and Manufacturing Innovation, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Electrical, Communications, and Cyber Systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分布式添加剂制造具有有希望的潜力，可以连接和协调单个制造商，以进行高效的按需生产。它可以利用众多添加剂制造商的自由形式制造，形成灵活且稳健的供应链，并在将来实现可重构的大规模定制。但是，这些分布式制造商的产品质量，一致性和隐私问题在充分释放分布式添加剂制造的潜力方面构成了巨大的挑战。这项未来的制造种子赠款（FMSG）网络制造项目将支持基础研究，以提供开发统一算法和培训框架的所需知识。名为FEDMDL的新框架将奠定坚实的基础，以在隐私，洞察力共享的制造网络中实现一致可靠的生产。这将进一步促进在航空航天，汽车，医疗保健等各个行业中采用添加性生产的零件，并将促进中小型制造商参与国家供应链。因此，这项研究的结果将使美国制造业和经济的竞争优势受益。这项研究为制造公司提供了新型机器学习和联合计算技术的协同作用。多学科的方法将有助于扩大代表性不足的群体参与研究的参与，并积极影响工程教育。 统一的算法和培训框架FEDMDL将绘制一条新的理论途径，以实现可靠的生产，一致的质量和保护隐私的数据共享，以分布式添加剂制造。 FEDMDL将将添加剂制造过程的基本物理学综合为深度学习算法，并在联合学习网络基础架构上训练新模型。在此种子赠款中，FEDMDL将对分布式制造网络中的添加性生产金属进行疲劳性能评估。研究团队将：（1）进行疲劳测试和缺陷表征，以了解材料 - 缺陷 - 几何 - 加载效果关系； （2）开发以裂缝力学为中心的深度学习模型，以近似多物理多物理多尺度过程，并预测多轴负载下复杂几何形状的疲劳性能； （3）设计一个跨性别，添加剂制造的联合学习网络基础设施，以通过制造商的稀疏，孤立的数据集的集体见解来培训深度学习模型； （4）通过将其部署在现实世界分布式添加剂制造网络中来评估框架。这项工作将导致一个经过实验验证的，可概括的算法和培训框架，以促进与集体智能的未来分布式添加剂制造中的质量建模，资格和控制中的应用。该项目由民事部门共同资助。机械和制造创新，启发竞争研究的既定计划（EPSCOR）以及电气，通信和网络系统的部门。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。