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.
Based 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.
The 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.
A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.
This project is supported by National Natural Science Foundation of China (Grant No. 51875362) and partly supported by SAIC General Motors Cooperation Limited.
Liu, Y., Zhang, S. and Chu, G. (2019), "Combination modeling of auto body assembly dimension propagation considering multi-source information for variation reduction", Assembly Automation, Vol. 39 No. 4, pp. 514-522. https://doi.org/10.1108/AA-05-2018-074Download as .RIS
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