In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans' actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators' actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on a reducer assembling line show the effectiveness of the proposed method. The research is expected to provide a guidance for operators to correct their actions to reduce the cost of quality defects and improve the efficiency of workforce.
Z. Wang et al., "Vision Sensor based Action Recognition for Improving Efficiency and Quality under the Environment of Industry 4.0," Procedia CIRP, vol. 80, pp. 711-716, Elsevier B.V., May 2019.
The definitive version is available at https://doi.org/10.1016/j.procir.2019.01.106
26th CIRP Conference on Life Cycle Engineering, LCE 2019 (2019: May 7-9, Lafayette, IN)
Engineering Management and Systems Engineering
Keywords and Phrases
Action recognition; Convolutional neural network; Hierarchical clustering; Real-time monitoring
International Standard Serial Number (ISSN)
Article - Conference proceedings
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