Data-driven Modeling-based Variation Analysis for the Compliant Sheet Metal Assemblies

doi:10.3901/JME.2019.10.034

Abstract

Abstract: Variation analysis in a compliant sheet metal assembly process is of great significance for eliminating assembly quality faults. Existing analytical modeling methods are not competent because they are limited by material, geometry and assembly process of the compliant sheet metal parts. Different from the analytical modeling method, a data-driven modeling-based method for variation analysis in a compliant sheet metal assembly process is proposed based on the measured historical data of key product characteristics. Through engineering experience and mathematical deduction, the multivariate first-order autoregressive model and the multivariate partial linear model are established. The maximum likelihood estimation method and the least squares kernel smoothing estimation method are employed to calculate the parameters and nonparametric estimates of the established models. A typical four-step hood assembly example with four key product characteristics demonstrates the effectiveness of the proposed method in the process of variation analysis. The data-driven modeling method is easy to model and the analysis results are accurate and reliable, which provides a new path for variation analysis of sheet metal assembly process.

Key words: compliant sheet metal assembly, first-order autoregressive model, least squares kernel smoothing estimation, partially linear model, variation analysis

CLC Number:

TH161

ZHANG Lei, HUANG Chuanhui, ZHU Enxu, WANG Lei, DONG Yan. Data-driven Modeling-based Variation Analysis for the Compliant Sheet Metal Assemblies[J]. Journal of Mechanical Engineering, 2019, 55(10): 34-41.

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