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

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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.