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

分布式添加剂制造在连接和协调各个制造商以实现高效的按需生产方面具有很大的潜力。它可以利用众多添加剂制造商的自由制造来形成灵活而稳健的供应链，并在未来实现可重构的大规模定制。然而，这些分布式制造商之间的产品质量、一致性和隐私问题对充分释放分布式添加剂制造的潜力构成了巨大的挑战。未来制造种子资助(FMSG)网络制造项目将支持基础研究，为开发统一的算法和培训框架提供必要的知识。这个名为FEDMDL的新框架将为在保护隐私、洞察共享的制造网络中实现一致和可靠的生产奠定坚实的基础。这将进一步促进航空航天、汽车、医疗保健等各行业采用附加制造部件，并将促进中小企业参与国家供应链。因此，这一研究结果将有利于美国制造业和经济的竞争优势。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。多学科方法将有助于扩大代表性不足的群体对研究的参与，并对工程教育产生积极影响。统一的算法和培训框架FEDMDL将为实现分布式添加剂制造中可靠的生产、一致的质量和隐私保护的数据共享开辟一条新的理论道路。FEDMDL将把添加剂制造过程的基本物理学合成到深度学习算法中，并在联邦学习网络基础设施上训练新模型。在这项种子拨款中，FEDMDL将以分布式制造网络中添加制造的金属的疲劳性能评估为原型。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-疲劳的关系；(2)开发以断裂力学为中心的深度学习模型，以近似多物理多尺度过程，并预测复杂几何形状在多轴载荷下的疲劳性能；(3)设计跨竖井、支持添加制造的联合学习网络基础设施，以从制造商的稀疏、孤立的数据集中训练深度学习模型；以及(4)通过将其部署在现实世界的分布式添加剂制造网络中来评估该框架。这项工作将产生一个经过实验验证的、可推广的算法和培训框架，以催化未来具有集体智能的分布式添加剂制造在质量建模、资格鉴定和控制方面的研究和应用。该项目由民用部门联合资助。机械和制造创新，激励竞争研究的既定计划(EPSCoR)，以及电气、通信和网络系统部门。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.