In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power close to the data source in order to achieve real-time recognition. For data collection, the operator's activity is captured using visual cameras from different perspectives. For operation recognition, instead of directly building and training a deep learning model from scratch, which needs a huge amount of data, transfer learning is applied to transfer the learning abilities to our application. A worker assembly operation dataset is established, which at present contains 10 sequential operations in an assembly task of installing a desktop CNC machine. The developed transfer learning model is evaluated on this dataset and achieves a recognition accuracy of 95% in the testing experiments.
W. Tao et al., "Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing,", vol. 48, pp. 926 - 931 Elsevier, Jan 2020.
The definitive version is available at https://doi.org/10.1016/j.promfg.2020.05.131
48th SME North American Manufacturing Research Conference, NAMRC 48 (Cancelled due to COVID-19)
Mechanical and Aerospace Engineering
Engineering Management and Systems Engineering
Keywords and Phrases
Artificial Intelligence; Fog Computing; Intelligent Manufacturing; Operation Recognition; Smart Manufacturing
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Jan 2020