Quality inspection scheme for automotive laser braze joints

  • Weicheng Wang
  • Yan CaiEmail author
  • Hui-Ping WangEmail author
  • Blair E. Carlson
  • Michael Poss


Laser triangulation and image processing are two major methods for surface quality inspection. The former has the advantage of three-dimensional information, while the latter can provide more detailed characteristics. However, there are few researches on effective integration of the two methods. In this paper, a lab scale laser braze quality inspection system utilizing information from laser triangulation scanning and optical image acquisition was able to inspect an automotive laser braze at a scanning speed of 1.5 m/min with 100% accuracy. The process started with defect zoning to sort out braze areas with and without defect based on the joint surface profile reconstructed from laser triangulation. Feature extraction was then carried out on the images of defect-containing braze areas, in which the location, size, geometry, and texture features of the defects were extracted from the gray-scale images, and since these features were extracted from only defect-containing areas, a lot of computational cost was saved, which reduced time consumed greatly. For defect classification, in the training stage the extracted features were processed by the principal component analysis and the first three principal components were calculated for 5 typical defects: surface cavity, pore, hump, burn-through, and lack-of-braze. In the test stage, the first three principal components of the images of interest were calculated and compared with the established principal component values of the 5 defect types for defect classification.


On-line inspection Braze surface quality Laser triangulation Image processing Principal component analysis (PCA) 



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Materials Laser Processing and ModificationShanghai Jiao Tong UniversityShanghaiChina
  2. 2.General Motors CompanyWarrenUSA

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