Fitting optimization based on weighted Gaussian imaging method for auto body taillight assembly
Abstract
Purpose
Fitting evenness is one key characteristic for three-dimensional objects' optimal fit. The weighted Gaussian imaging method is developed for fitting evenness of auto body taillight fitting optimization.
Design/methodology/approach
Fitting boundary contours are extracted from scanning data points. Optimal fitting target is represented with gap and flushness between taillight and auto body. By optimizing the fitting position of the projected boundary contours on the Gaussian sphere, the weighted Gaussian imaging method accomplishes optimal requirements of gap and flushness. A scanning system is established, and the fitting contour of the taillight assembly model is extracted to analyse the quality of the fitting process.
Findings
The proposed method accomplishes the fitting optimization for taillight fitting with higher efficiency.
Originality/value
The weighted Gaussian imaging method is used to optimize the taillight fitting. The proposed method optimized the fitting objects' 3-D space, while the traditional fitting methods are based on 2-D algorithm. Its time complexity is O(n3), while those of the traditional methods are O(n5). The results of this research will enhance the understanding of the 3-D optimal fitting and help in systematically improving the productivity and the fitting quality in automotive industry.
Keywords
Acknowledgements
The authors are grateful for the support of this work by a grant from the National Natural Science Foundation of China (50905117), the Ministry of Science and Technology of China, under the contract of No. 2010 DFA72760 and SMC-Chenxing Scholar Program of Shanghai Jiao Tong University. The authors are also grateful to Mr Qiang Chen who conducted the experiment and provided some data and materials for the paper.
Citation
Gao, X., Wang, H. and Chen, G. (2014), "Fitting optimization based on weighted Gaussian imaging method for auto body taillight assembly", Assembly Automation, Vol. 34 No. 3, pp. 255-263. https://doi.org/10.1108/AA-02-2014-015
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited