Transforming distributed additive manufacturing
By Alyssa Turner
Jia (Peter) Liu, assistant professor of industrial and systems engineering, is the principal investigator of a multi-disciplinary team of scientists that was awarded a $498,762 grant by the National Science Foundation for their project, “Federated Deep Learning for Future Ubiquitous Distributed Additive Manufacturing.”
This Future Manufacturing Seed Grant project introduces developing a unified algorithmic and training framework, Federated Modular Deep Learning (FedMDL), for future distributed additive manufacturing (AM). FedMDL will ensure reliable production, consistent qualification and privacy-preserving information sharing at two levels: algorithmic and cyber-infrastructural. Nima Shamsaei, Philpott-Westpoint Stevens Distinguished Professor of mechanical engineering is a co-PI.
According to Liu, the potential benefits of distributed AM in the supply chain are driving research efforts in the U.S. and across the globe.
“The 3D printing COVID-19 rapid response initiative in the U.S. provided nearly one million pieces of safe personal protective equipment for local medical providers,” said Liu. “But the unexpected debut of nationwide AM production revealed outstanding challenges FedMDL is targeting to fix.”
By addressing the challenges of unreliable, consistent product quality and the inability to share information and insights without compromising privacy, FedMDL is charting a new course to achieve reliable and reconfigurable nationwide mass customization capabilities by distributed AM.
“Once the FedMDL framework is developed and validated, it will be open-sourced for the AM community and other manufacturing communities to expand their research to a distributed manufacturing paradigm,” said Liu.
