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Investigation of surface defects for extruded aluminium profiles using pattern recognition techniques

Student thesis: Doctoral Thesis

  • Apostolos Chondronasios
This research investigates detection and classification of surface defects in extruded aluminium profiles in order to replace the traditional eye inspection which is still themethod widely used today. Through an extensive literature review it is evident that extruded aluminium surface is not investigated properly, while similar industrial products such as copper strips or rolled steel have attracted more interest. An experimental machine vision system is used to capture images from surfaces of extruded aluminium profiles. Extensive feature selection is investigated and appropriate statistical features from a novel technique based on Gradient-Only Co-occurrence Matrices are used to detect and classify defects. The methodology created in this research, makes use of the Sobel edge detector to obtain the gradient magnitude of the image and is followed by the extraction of statistical texture measures from the gradient, after a transformation of the gradient values. Comparisons are made between the statistical features extracted from the original image (Gray-Level Co-occurrence Matrix) and those extracted from the gradient magnitude using a novel approach (Gradient-Only Co-occurrence Matrix). The features extracted from the image
processing are classified by feed-forward artificial neural networks. Experiments were conducted for a three class and a four class case study, with the first consisting of Good Surface, Blisters and Scratches, and the second introducing Die Lines to the classes of the first case study. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped, showing better classification accuracy for the novel technique (98.9%) compared to research standard methodology of gray-level co-occurrence matrices (55.9%).
Original languageEnglish
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Award dateMay 2015

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