FMRG: Manufacturing USA: Cyber: Privacy-Preserving Tiny Machine Learning Edge Analytics to Enable AI-Commons for Secure Manufacturing

点击翻译按钮获取中文摘要

Current manufacturing systems based on a global supply chain work well, producing high-volume products at low cost. However, they are fragile to perturbations (e.g., COVID-19) and even processes that are essential to success, such as new product development and manufacturing ramp-up and optimization, can be prohibitively lengthy and expensive. Artificial Intelligence (AI) has revolutionized many industries and can be an important tool to improve the productivity and agility of manufacturing, but progress has been slow in manufacturing. This Future Manufacturing Research Grant (FMRG) Manufacturing USA: CyberManufacturing project is a fundamental reimagination of distributed AI/ML techniques to transform the future of manufacturing by establishing an AI-Commons that bridges multiple sites and companies using secure, distributed machine learning (ML), incentivized information sharing, and continuous quality improvement and training. This addresses a critical shortcoming of the current approach to AI in manufacturing; the limitation of training data to in-company data. Since AI algorithms increase in power with more data, secure data sharing and aggregation has the potential to provide vastly better AI solutions to all manufacturers. The project will also research the edge computation hardware needed to execute the resulting algorithms on the factory floor. The team will work closely with Ivy Tech Community College and the Vertically Integrated Projects program, which helps students from Purdue, Harvard, and Tuskegee University work on industry-defined manufacturing AI projects. This project’s goal will be achieved by fulfilling the following four objectives: (1) Co-optimization of Tiny Machine Learning (TinyML) hardware and software for manufacturing; (2) Design of privacy and confidentiality policies for an AI-Commons that encourages and incentivizes knowledge sharing; (3) Demonstration of data aggregation and predictive technical cost modeling for some foundational manufacturing processes; and (4) Introduction of AI in manufacturing curricula and integration with workforce development. A key focus is on AI system deployment to obtain necessary data for optimization of ML algorithms for common manufacturing processes in pharmaceutical, food processing, job shop machining, and hybrid composite materials. The project team has developed a strong partnership with small and large manufacturers in the Wabash Heartland Innovation Network (WHIN) region in Indiana. TinyML devices will be deployed throughout this region, where the sharing of data and AI could improve operations significantly.This project is jointly funded by the Division of Civil. Mechanical and Manufacturing Innovation, the Division of Computer and Network Systems, the Division of Engineering Education and Centers and the Office of Multidisciplinary Activities of the Directorate for Social, Behavioral and Economic Sciences.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.