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research-article

Individualizing Locator Adjustments of Assembly Fixtures Using a Digital Twin

[+] Author and Article Information
Abolfazl Rezaei Aderiani

SE- 41296 Gothenburg Horsalvägen 7B Gothenburg, 41296 Sweden aderiani@chalmers.se

Kristina Wärmefjord

Product and Production Development Göteborg, SE-41296 Sweden kristina.warmefjord@chalmers.se

Rikard Söderberg

Product and Production Development PU Göteborg, Vastra gotaland SE 41296 Sweden rikard.soderberg@chalmers.se

Lars Lindkvist

Dept. of Product and Production Development horsalsvagen 7 Göteborg, Vastra gotaland SE-41296 Sweden lali@chalmers.se

1Corresponding author.

Manuscript received October 31, 2018; final manuscript received March 27, 2019; published online xx xx, xxxx. Assoc. Editor: Bin He.

ASME doi:10.1115/1.4043529 History: Received October 31, 2018; Accepted March 27, 2019

Abstract

Implementing the concept of a Digital Twin in full production provides enough data on each individual assembly for real-time control of production processes. Taking advantage of this opening, this paper proposes individualized locator adjustments as a new method to improve the geometrical quality of assemblies. In this method, all locators in the assembly fixture can be adjusted for each individual assembly based on the scanned data of the mating parts of that assembly. The optimal adjustment of every locator for each individual assembly is obtained using an optimization algorithm and non-rigid variation simulation tools (computer aided tolerancing tools). This method is applied to three industrial cases and geometrical variations and the mean deviation from nominal positions are compared to non-individualized adjustments and also when there are no adjustments. The results show that applying this method, an improvement of up to 81% in geometrical variation and 78% in the mean deviation of assemblies can be obtained compared to assemblies without adjustments. These improvements are 60% and 57% higher than non-individualized adjustments of locators for the variation and the mean deviation, respectively. Moreover, a modification on the optimization algorithm has been proposed that reduces the amount of required adjustments.

Copyright © 2019 by ASME
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