Smart manufacturing integrates information, technology, and human ingenuity to inspire the next revolution in the manufacturing industry. Manufacturing has been identified as a key strategic investment area by the U.S. government, private sector, and university leaders to spur innovation and keep America competitive. However, the lack of new methodologies and tools is challenging continuous innovation in the smart manufacturing industry. This award supports fundamental research to develop a cyber-physical sensing, modeling, and control infrastructure, coupled with augmented reality, to significantly improve the efficiency of future workforce training, performance of operations management, safety and comfort of workers for smart manufacturing. Results from this research are expected to transform the practice of worker-machine-task coordination and provide a powerful tool for operations management. This research involves several disciplines including sensing, data analytics, modeling, control, augmented reality, and workforce training and will provide unique interdisciplinary training opportunities for students and future manufacturing engineers. An effective way for manufacturers to tackle and outpace the increasing complexity of product designs and ever-shortening product lifecycles is to effectively develop and assist the workforce. Yet the current management of manufacturing workforce systems relies mostly on the traditional methods of data collection and modeling, such as subjective observations and after-the-fact statistics of workforce performance, which has reached a bottleneck in effectiveness. The goal of this project is to investigate an integrated set of cyber-physical system methods and tools to sense, understand, characterize, model, and optimize the learning and operation of manufacturing workers, so as to achieve significantly improved efficiency in worker training, effectiveness of behavioral operations management, and safety of front-line workers. The research team will instrument a suite of sensors to gather real-time data about individual workers, worker-machine interactions, and the working environment,develop advanced methods and tools to track and understand workers' actions and physiological status, and detect their knowledge and skill deficiencies or assistance needs in real time. The project will also establish mathematical models that encode the manufacturing process in the research sensing and analysis framework, characterize the efficiency of worker-machine-task coordination, model the learning curves of individual workers, investigate various multi-modal augmented reality-based visualization, guidance, control, and intervention schemes to improve task efficiency and worker safety, and deploy, test, and conduct comprehensive performance assessments of the Researched technologies.