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Morphological detection and feature-based classification of cracked regions in ferrites

100%

Nieniewski M. , Chmielewski L. , Jóźwik A. , Skłodowski M.

tom Vol. 8, No. 4

699-712

Automatic quality inspection of ferrite products is difficult as thier surfaces are dark and in many cases coverred with traces of grinding. A two-stage vision system for detection and measurement of crack regions was devised. In the first stage the regions with strong evidence for cracks are found using a morphological detector of irregular grightness changes with subsequent morphological reconstruction. In the second stage the feature-based k-Nearest Neighbors classifier analyzes the pixel indicated in the first stage. The classifier is optimized by using procedures of reclassification and replacement carried out on the reference set of pattern pixels to achieve a low error rate and a maximum speed of computation.

Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy

84%

Mishra A.

tom Vol. 4, No 1

27--32

The research reported in this paper focuses on the application of local binary patterns (LBPs) for surface defects detection. The surface defection detection algorithm for friction stir welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the local binary patterns method can be used for real-time surface defects detection in friction stir welded joints.