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Shop floor data-driven spatial–temporal verification for manual assembly planning

  • Wei FangEmail author
  • Lianyu Zheng
Article
  • 52 Downloads

Abstract

Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and actual assembly actions would result in the failure of the manual assembly planning. Therefore, the verification for the manual assembly planning is significant to ensure the correctness of the actual assembly operations, performing a convergence between the cyber and physical world. The challenges involved in retrieving and utilizing the actual data about the manual activities on a shop floor. In this paper, a self-contained wearable tracking system is proposed and applied to collect the shop-floor data during the manual assembly operations. And then, an unsupervised classification method is applied to empower semantic knowledge to the shop-floor data derived from the workplace. Thus, an automatic spatial–temporal verification for manual assembly planning is carried out, providing indicators to optimize the current manual assembly planning. Experimental results illustrate that the proposed work can perform the spatial–temporal verification for manual assembly task and indicate evidence to improve the manual assembly planning objectively.

Keywords

Spatial–temporal verification Manual assembly planning Data-driven Shop floor Wearable system 

Notes

Acknowledgements

The authors gratefully acknowledge the supports of the National Key Research and Development Program of China (No. 2018YFB1701602), the Defense Industrial Technology Development Program (JCKY2018601C011), the Fundamental Research Funds for the Central Universities (2019RC26) and Beijing Engineering Research Center of Pose Intelligent Equipment. We also appreciate Xu Jiaxing for preparing the manual assembly experiments.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of AutomationBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  2. 2.School of Mechanical Engineering and AutomationBeihang UniversityBeijingPeople’s Republic of China

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