A Self-Aware and Active-Guiding Training & Assistant System for Worker-Centered Intelligent Manufacturing
Training and on-site assistance is critical to help workers master required skills, improve worker productivity, and guarantee the product quality. Traditional training methods lack worker-centered considerations that are particularly in need when workers are facing ever-changing demands. In this study, we propose a worker-centered training & assistant system for intelligent manufacturing, which is featured with self-awareness and active-guidance. Multi-modal sensing techniques are applied to perceive each individual worker and a deep learning approach is developed to understand the worker's behavior and intention. Moreover, an object detection algorithm is implemented to identify the parts/tools the worker is interacting with. Then the worker's current state is inferred and used for quantifying and assessing the worker performance, from which the worker's potential guidance demands are analyzed. Furthermore, onsite guidance with multi-modal augmented reality is provided actively and continuously during the operational process. Two case studies are used to demonstrate the feasibility and great potential of our proposed approach and system for applying to the manufacturing industry for frontline workers.
W. Tao et al., "A Self-Aware and Active-Guiding Training & Assistant System for Worker-Centered Intelligent Manufacturing," Manufacturing Letters, vol. 21, pp. 45-49, Elsevier Ltd, Aug 2019.
The definitive version is available at https://doi.org/10.1016/j.mfglet.2019.08.003
Mechanical and Aerospace Engineering
Engineering Management and Systems Engineering
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Augmented reality; Cyber-physical system; Deep learning; Intelligent manufacturing; Smart manufacturing
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Society of Manufacturing Engineers (SME), All rights reserved.
01 Aug 2019