Variation Model and Analysis of Spatial Assembly with Multiple Closed Chains

Vincent McKenna, Yan Jin, Adrian Murphy, Michael Morgan, Caroline McClory, Colm Higgins, Rory Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Process variations are inherent in all manufacturing processes and have a profound effect on the quality and cost of assemblies. Modelling variation propagation through complex assemblies is difficult due to the abundance of potential sources, disparate magnitudes and the stochastic nature of variation occurrence. Robust methods for modelling simple connective assemblies have been established in existing research. Recent work has started to consider spatial assemblies with one closed chain, where parts are located using fixtures. But the study of spatial assemblies with multiple closed chains is still lacking, where constraints are shared by multiple chains. Achieving all Key Characteristics (KCs) simultaneously is not possible when variations are considered. This creates a major challenge for process planning and optimization as additional processes at the assembly stage are necessary, influencing production time, cost and quality.
This paper proposes for the first time a method of modelling variation propagation in a spatial assembly with multiple closed chains. The benefit of the methodology is the ability to understand variation drivers and their transmission mechanism, to enable informed process decisions at a full system level. An algorithm for quantification of assembly KCs is proposed, which allows variation sensitivity on assembly KCs to be clearly identified.
Original languageEnglish
Title of host publicationAdvances in Manufacturing Technology XXXI
Subtitle of host publicationProceedings of the 15th International Conference on Manufacturing Research
EditorsJames Gao, Mohammed El Souri, Simeon Keates
PublisherIOS Press
Pages555-560
Volume6
DOIs
Publication statusPublished - 05 Sep 2017

Keywords

  • Multiple Closed Chains
  • Variation Analysis
  • Key Characteristics

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