期刊
JOURNAL OF MANUFACTURING SYSTEMS
卷 55, 期 -, 页码 69-81出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2020.02.010
关键词
Intellegent manufacturing; Augmented reality; Deep learning; Object detection; Region-based Convolutional Neural Networks (R-CNN); Mechanical assembly
资金
- National Science Foundation [CMMI-1646162]
- Intelligent Systems Center at Missouri University of Science and Technology
Quality and efficiency are crucial indicators of any manufacturing company. Many companies are suffering from a shortage of experienced workers across the production line to perform complex assembly tasks. To reduce time and error in an assembly task, a worker-centered system consisting of mull-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The integrated AR is designed to provide on-site instructions including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools and displayed onto a 2D scene without using real tool images. By experimenting the system to a mechanical assembly of a CNC carving machine, the result of a designed experiment shows that the system helps reduce the time and errors of the given assembly tasks by 33.2 % and 32.4 %, respectively. With the integrated system, an efficient, customizable smart AR instruction system capable of sensing, characterizing requirements, and enhancing worker's performance has been built and demonstrated.
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