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Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : Expert Systems with Applications
Source : Expert Systems with Applications, Volume 38, Number 4, p.3260-3266 (2011)
Keywords : Algorithms, Automated visual inspection, Bayes algorithms, C4.5 algorithm, Classification accuracy, Classifier algorithms, Classifiers, Computer vision, Cracks, Decision trees, Design process, Feature extraction, Feature extraction and classification, Graphic methods, Histogram features, Inspection, Learning systems, Machine design, Machine vision, Machine vision systems, Machine-learning, Shafts (machine components), Visual inspection, Welding quality
Campus : Coimbatore, Amritapuri
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering, Center for Computational Engineering and Networking (CEN)
Year : 2011
Abstract : Visual inspection on the surface of components is a main application of machine vision. Visual inspection finds its application in identifying defects such as scratches, cracks bubbles and measurement of cutting tool wear and welding quality. Machine learning approach to machine vision helps in automating the design process of machine vision systems. This approach involves image acquisition, preprocessing, feature extraction and classification. Study shows a library of features, and classifiers are available to classify the data. However, only the best combination of them can yield the highest classification accuracy. In this study, images with different known conditions were acquired, preprocessed, and histogram features were extracted. The classification accuracies of C4.5 classifier algorithm and Naïve Bayes algorithm were compared, and results are reported. The study shows that C4.5 algorithm performs better. © 2010 Elsevier Ltd. All rights reserved.
Cite this Research Publication : Sa Ravikumar, Dr. K. I. Ramachandran, and Sugumaran, Vb, “Machine learning approach for automated visual inspection of machine components”, Expert Systems with Applications, vol. 38, pp. 3260-3266, 2011.