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

Wei Fang () and Lianyu Zheng
Additional contact information
Wei Fang: Beijing University of Posts and Telecommunications
Lianyu Zheng: Beihang University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 13, 1003-1018

Abstract: 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 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-019-01491-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01491-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-019-01491-y

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().