Credit: CTL

In April 2024, NIST researcher Dr. M. Sharp published a Manufacturing Extension Partnership (MEP) blog post, NIST Explores AI-Enhanced Monitoring in Manufacturing Processes, which has engaged both MEP members and broader public stakeholders. Building from this success, Dr. Sharp is coordinating with MEP to develop an upcoming series of similar blog posts to bring further attention and community interactions on this important topic.

In a world where precision, reliability, and efficiency are paramount, manufacturing processes must evolve to take advantage of new capabilities based on Artificial Intelligence (AI). In this inaugural post, Dr. Sharp delves into the realm of AI-enhanced monitoring in manufacturing and describes how NIST is working to more actively support this domain.

For this effort, NIST needs access to high fidelity, broad scope manufacturing data streams that mimic the faults, flaws, and eccentricities that are the staple of real-world manufacturing. Thus, the Industrial Artificial Intelligence Metrology and Management (IAIMM) team collaborated with a NIST Cybersecurity for Operational Technologies team to modify and update the Collaborative Robotic Operations Workcell (CROW) to make a robust and broad scope source of data feasible on a benchtop setup.

CROW was created to facilitate the evaluation of solutions across entire manufacturing systems, from digital communications, to product quality and human interactions. CROW is a multistage manufacturing operation, featuring robotic arms orchestrating the production and evaluation of continuous cyclic product streams. Equipped with 10 major physical components, including collaborative robots, conveyor belts, inspection cameras, and a suite of sensors and digital loggers, CROW provides a comprehensive platform for testing and refining AI-driven solutions in a safe, controlled environment. Anomaly detection and process error prevention are some of the challenges CROW will address by enabling creation and evaluation of tools and procedures to detect and mitigate issues before they escalate.

Through open-access data produced by CROW, researchers, developers, and manufacturers will be able to harness domain-appropriate data streams for the development and testing of AI-enhanced industrial technologies, including development of best practices and standard operating procedures. As stakeholders navigate the intricate landscape of manufacturing, the NIST team is looking to promote collaboration, standardization, and trust. For collaboration opportunities or further information, please contact Dr. M. Sharp in the Smart Connected Systems Division at NIST.