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Critical component detection in assemblies: a graph centrality approach

Published online by Cambridge University Press:  16 May 2024

Robert Ballantyne,
Adam McClenaghan,
Oliver Schiffmann and
Chris Snider
Robert Ballantyne*
Affiliation:
University of Bristol, United Kingdom
Adam McClenaghan
Affiliation:
University of Bristol, United Kingdom
Oliver Schiffmann
Affiliation:
University of Bristol, United Kingdom
Chris Snider
Affiliation:
University of Bristol, United Kingdom
*
robert.ballantyne@bristol.ac.uk

Abstract

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This study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

Keywords

network analysisdigital manufacturingmodellingtolerance representation and management

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024, pp. 1929 - 1938
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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