Continuous process verification for trustworthy manufacturing

The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded the Board of Regents of the University of Nebraska $307,868 on January 1, 2027, to develop a control-monitoring co-randomization framework for trustworthy manufacturing under the Engineering program (CFDA 47.041).

The research advances manufacturing system integrity by enabling continuous verification that sensed data faithfully represent physical processes, addressing risks posed by cyberattacks, equipment failures, and sensing errors that compromise production quality, increase waste, disrupt operations, or create safety hazards. The project investigates whether carefully designed stochastic control and monitoring actions can generate physical watermarks revealing discrepancies in process measurements. The framework integrates randomized control strategies, adaptive process monitoring, statistical signal analysis, system identification, and predictive control to continuously validate process integrity while preserving production quality and operational efficiency, and will be evaluated on representative manufacturing platforms.

Work is performed in Lincoln, Nebraska, with a period of performance from January 1, 2027, through December 31, 2028. The University of Nebraska, operating through its Office of Sponsored Programs Division, is the recipient.

Abstract

This project supports research to advance the trustworthiness of manufacturing systems. Manufacturing is undergoing a rapid transformation as factories increasingly rely on automation, intelligent decision making, and data-driven optimization to improve productivity, quality, and resilience. These advances depend on trustworthy systems that monitor and control physical manufacturing processes. If process measurements are compromised by cyberattacks, equipment failures, or sensing errors, manufacturers may unknowingly make incorrect decisions that reduce product quality, increase waste, disrupt production, and create safety risks. However, today?S manufacturing systems are designed primarily to maximize production efficiency rather than continuously verify the integrity of process information. This project addresses this fundamental challenge by enabling manufacturing systems to continuously verify that sensed data faithfully represent physical processes. The resulting advances will improve the security, resilience, and reliability of advanced manufacturing, facilitate the adoption of intelligent manufacturing technologies, educate the future workforce, and strengthen the nation?S economic competitiveness. The specific goal of the research is to develop a control-monitoring CO-randomization framework for trustworthy manufacturing. The research investigates whether carefully designed stochastic control and monitoring actions can generate physical watermarks that reveal discrepancies caused by cyberattacks, equipment faults, or compromised sensing. The framework integrates randomized control strategies, adaptive process monitoring, statistical signal analysis, system identification, and predictive control to continuously validate process integrity while preserving production quality and operational efficiency. The framework will be evaluated on representative manufacturing platforms, including automated assembly systems and chemical production processes. By tightly coupling physical process dynamics with cybersecurity mechanisms, this research establishes the scientific foundations for manufacturing systems that can continuously validate the trustworthiness of process information, enabling more reliable automation and the next generation of intelligent manufacturing. 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.- subawards are not planned for this award.