Canzini, E., Auledas, M., Chasteau, D. et al. (1 more author) (2022) A novel sensing template using data fusion for large volume assembly. In: IFAC-PapersOnLine. 14th IFAC Workshop on Intelligent Manufacturing Systems IMS 2022, 28-30 Mar 2022, Tel-Aviv, Israel. Elsevier BV , pp. 283-288.
Abstract
The size of large components within manufacturing processes leads to complications with automating the processes required to assemble them into larger structures. In recent years, development of multi-sensor networks and breakthroughs in measuring algorithms have allowed for the creation of novel methods of mating large components. One major challenge with deploying sensor networks into production environments is the ability to attach sensors to large volume components. This can be remedied with the use of a sensing template that acts as a pseudo-virtual jig for the assembly process where sensors are embedded onto the template, thus not interfering with the physical assembly. The key step for this sensing template is creating an algorithmic process for accurate component localisation. This paper will introduce an innovative method of using data fusion attached to a sensing template embedded in an aerospace assembly process. A sensing algorithm utilising a Kalman filter allows for accurate component mating with a low error offset and high repeatability. The results of the sensing template show how it is capable of reducing the error offset and improves the repeatability of measurements.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an open access article under the CC BY-NC-ND license. (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Autonomy; Manufacturing; Automation; Internet-of-Things; Sensing Enterprise |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ROYAL ACADEMY OF ENGINEERING (THE) RCSRF1718\5\41 Engineering and Physical Sciences Research Council 2607286 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Aug 2023 16:06 |
Last Modified: | 24 Aug 2023 16:06 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ifacol.2022.04.207 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202710 |