Cyber-Physical Smart Manufacturing

Sponsors: National Science Foundation

 

Description

The lack of cyber-physical system methodologies and resources is challenging continuous progress in the modern manufacturing industry. This project is aimed at performing fundamental research to develop a cyber-physical sensing, simulation and control infrastructure, combined with augmented reality, to greatly enhance the quality of workforce training, the success of facilities management, and the safety and comfort of manufacturing employees. The research results of this project include multi-modal recognition of worker activity, construction of a fusing and refining convolutional neural network model for assembly action recognition, and an augmented reality instructional system for intelligent manual assembly.
  1. “Smart Augmented Reality Instructional System for Mechanical Assembly Towards Worker-Centered Intelligent Manufacturing," Z-H. Lai, W. Tao, M. C. Leu, and Z. Yin, Journal of Manufacturing Systems, Vol. 55, Apr. 2020, pp. 69-81.
  2. “Fusing and Refining Convolutional Neural Network Models for Assembly Action Recognition in Smart Manufacturing,” M. Al-Amin, R. Qin, W. Tao, D. Doell, R. Lingard and M. C. Leu, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Jun. 2020, pp. 1-14.
  3. “Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing," W. Tao, M. C. Leu, and Z. Yin, Engineering Applications of Artificial Intelligence, Vol. 95, Oct. 2020.
  4. “Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals," W. Tao, M. C. Leu, Z. Yin, and R. Qin, Information Fusion (under review).
  5. “Sensor Data Based Models for Workforce Management in Smart Manufacturing,” M. Al-Amin, R. Qin, W. Tao, and M. C. Leu, Proceedings of the 2018 Institute of Industrial and Systems Engineers Annual Conference (IISE 2018), May 19-22, 2018, Orlando, FL.
  6. “Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks,” W. Tao, Z. Lai, M. C. Leu and Z. Yin, Proceedings of the 46th SME North American Manufacturing Research Conference, (NAMRC 46), Jun. 18-22, 2018, College Station, TX.
  7. “A Region-Based Deep Learning Algorithm for Detecting and Tracking Objects in Manufacturing Plants,” M. M. Karim, D. Doell, R. Lingard, Z. Yin, M. C. Leu, and R. Qin, Proceeding of 25th International Conference on Production Research Manufacturing
  8. “Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion,” M. Al-Amin, W. Tao, D. Doell, R. Lingard, Z. Yin, M. C. Leu, and R. Qin, Proceeding of 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, Chicago, IL, Aug. 9–14, 2019.
  9. “Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing,” W. Tao, M. Al-Amin, H. Chen, M. C. Leu, Z. Yin, and R. Qin, Procedia Manufacturing, Vol. 48, Jun. 2020.