ManuFuture Today Network

Empower U.S. manufacturers to increase resiliency and international competitiveness by successfully adopting privacy-preserving, scalable, certify-as-build AI/ML practices to erase the digital-divide and accrue the benefit of economy-of-scale without centralization.

Purdue University's SMART Consortium received NSF Future Manufacturing and AnalytiXIN Grants focused on: Privacy preserving artificial intelligence for agile and resilient manufacturing. Led by Ali Shakouri, professor in the Elmore Family School of Electrical and Computer Engineering, the new project is in collaboration with colleagues at Harvard University, Tuskegee University and Ivy Tech Community College.

“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,” says Shakouri. “This project proposes a fundamental reimagination of distributed AI techniques by establishing an AI-Commons that bridges multiple sites and companies using secure, distributed machine learning (ML). The project will research the edge computation hardware (TinyML) needed to execute the resulting algorithms on the factory floor.”

Shakouri says 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 team has developed a strong partnership with small and large manufacturers in Indiana and expanding it to other states.

National Science Foundation Award # 2134667

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

Abstract

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, and job shop machining. 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.

Publications Produced

• Sánchez-Peña, Matilde and Vieira, Camilo and Magana, Alejandra J. "Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding" Computer Applications in Engineering Education, v.31, 2023 https://doi.org/10.1002/cae.22580
• Wang, Xihui and Shakouri, Ali and Ribeiro, Bruno and Chiu, George T.C. and Allebach, Jan P. "Active learning approaches to analysis of thin-film printed sensors for determining nitrate levels in soil" Electronic Imaging, v.35, 2023 https://doi.org/10.2352/EI.2023.35.15.COLOR-194
• Wang, Xihui and Mi, Ye and Shakouri, Ali and Chiu, George T.C. and Allebach, Jan P. "Improvements to color image and machine learning based thin-film nitrate sensor performance prediction: New texture features, repeated cross-validation, and auto-tuning of hyperparameters" Electronic Imaging, v.34 , 2022 https://doi.org/10.2352/EI.2022.34.15.COLOR-159
• Li, Adrian Shuai and Bertino, Elisa and Wu, Rih-Teng and Wu, Ting-Yan "Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation" 2023 IEEE International Conference on Industrial Technology (ICIT), 2023 https://doi.org/10.1109/ICIT58465.2023.10143099