Robust Gnome Percolation Model Segmentation for Automatic Visual Quality Inspection and NDT
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Abstract
Quality assurance is the systematic monitoring and evaluation of the various aspects of a product to maximize the probability that minimum standards of quality are being attained by the production process. Deviations from the normal quality that impair the operating characteristics of a metal or product and lead to a reduction in grade or to rejection of products should be considered as defects. To achieve zero defects (“Zero PPMâ€) output cost-effectively, manufacturers are making the commitment to move to online, automated Non Destructive Testing (NDT) methods. The proposed NDT method is to identify microscopic casting defects and cracks automatically, which may be even internal fault in nature and measure them by intelligent object detection and feature extraction tools. The paper introduces the Gnome Percolation Model based automated visual quality inspection and NDT employed using intelligent object detection and feature extraction in image processing as a tool in the automated visual inspection and NDT of finished products which have many advantages over existing methods .
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Keywords: GNOME; Automatic quality inspection; NDT; Percolation Segmentation
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