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Combination modeling of auto body assembly dimension propagation considering multi-source information for variation reduction

Combination modeling of auto body assembly dimension propagation considering multi-source... This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.Design/methodology/approachBased on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.FindingsThe combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.Originality/valueA combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

Combination modeling of auto body assembly dimension propagation considering multi-source information for variation reduction

Liu, Yinhua; Zhang, Shiming; Chu, Guoping
Assembly Automation , Volume 39 (4): 9 – Oct 3, 2019
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
DOI
10.1108/aa-05-2018-074
Publisher site
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Abstract

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.Design/methodology/approachBased on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.FindingsThe combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.Originality/valueA combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

Journal

Assembly Automation – Emerald Publishing

Published: Oct 3, 2019

Keywords: Combination modelling; Data-driven model; Mechanism model; Variation reduction

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