Cyber-Physical Sensing, Modeling, & Control with Augmented Reality for Smart Manufacturing Workforce Training & Operations Mgmt.
While U.S. manufacturers are investing tremendous efforts and resources to regain the power and growth of manufacturing, they are confronted by a set of critical and challenging issues on the workforce.
Specifically, (1) lack of workforce with advanced training and skills; (2) need for rapid, smart, and individualized training to achieve workforce agility; and (3) on-the-job personal assistance to improve worker performance, safety and comfort.
This project aims to address the above issues by exploring advanced CPS (cyber-physical system) methods and tools. Specifically, we will develop a cyber-physical sensing, modeling, and control infrastructure coupled with augmented reality to significantly improve the efficiency of workforce training, performance of operations management, safety and comfort of front-line workers for smart manufacturing.
The proposed project contributes to the CPS program vision, especially Technology for Cyber-physical Systems. It implements a key scientific principle of CPS that incorporates sensing, analysis, intervention and outcome measurements in a tightly coupled and dynamic loop.
Keywords: Technology for Cyber-Physical Systems, Smart Manufacturing, Human Sensing, Workforce Engineering, Augmented Reality
Intellectual Merit
Lack of knowledgeable, skilled and agile workforce is challenging the smart manufacturing industry. The goal of this project is to significantly improve the efficiency of workforce training, performance of operations management, safety and comfort of production workers for smart manufacturing. Powered by CPS techniques, the project will demonstrate a new pathway to sensing, training, and assisting each individual production worker. Specifically, the project has the following intellectual merits: (1) Sensing - we will instrument a suite of sensors to gather real-time data about individual workers, worker-machine interactions, and the working environment. (2) Analysis - from the sensor data, we will develop advanced methods and tools to track and understand workers’ actions and physiology status as well as to detect their knowledge or skill deficiencies or assistance needs in real-time. (3) Modeling - we will establish models that mathematically encode the manufacturing process in the proposed sensing and analysis framework, characterize the efficiency of worker-machine-task coordination and model the learning curves of individual workers. (4) Control and intervention - we will investigate various multi-modal augmented reality-based visualization, guidance, control, and intervention schemes to improve the task efficiency and worker safety. (5) Assessment - we will deploy, test, and conduct comprehensive performance assessment of the proposed CPS technologies in two application scenarios.
Broader Impact
Smart manufacturing, which is a U.S. strategic investment to spur innovations and keep America competitive, is facing the crisis of lack of capable workforce. The proposed CPS technology invents a promising solution to develop and engage a desired workforce as well as manage and guide them on-the- job. The project will advance the state-of-the-art in workforce training and operations management. We expect that this CPS design will transform the practice of worker-machine-task coordination and provide a powerful tool for operations management in smart manufacturing. This interdisciplinary research brings together experts from the areas of sensing, data analytics, modeling, control, augmented reality, workforce training and advanced manufacturing, representing an exciting and truly synergetic research effort. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving community college students, and engaging women and underrepresented minorities. The proposed technology will drastically improve the perception of the manufacturing industry being "clean, safe and high-tech" rather than "dirty, dangerous and boring," attracting millennials and younger generations to the ongoing transformation of smart manufacturing.
