Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

• Published in: Journal of materials research and technology Vol. 8; no. 1; pp. 1070 - 1083
• Main Authors: Ivo, Roberto Fernandes, Rodrigues, Douglas de Araújo, Santos, José Ciro dos, Freitas, Francisco Nélio Costa, Herculano, Luis Flaávio Gaspar, Abreu, Hamilton Ferreira Gomes de, Rebouças Filho, Pedro Pedrosa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-01-2019; Elsevier
• Subjects: Crystalline Orientation Distribution Function; Digital image processing; Electrical steel; Pattern recognition; Electrical steel; Pattern recognition; Crystalline Orientation Distribution Function; Digital image processing
• ISSN: 2238-7854
• DOI: 10.1016/j.jmrt.2018.05.028

This article discusses a fast and efficient classification of non-grain oriented electrical steel and its electromagnetic efficiency based on the analysis of the images of the Crystalline Orientation Distribution Function (CODF). The study was carried out on samples of a non-grain oriented electrical steel, semi-processed with 1.28% silicon, cold rolled with thickness reductions of 50.0% and 70.0%, and annealed at 730°C for 12h. The material was also subjected to annealing heat treatment for grain growth at temperatures of 620°C, 730°C, 840°C and 900°C for 1, 10, 100 and 1000min at each temperature. The database used was comprised of 32 images. The extractors Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Central Moments, Statistical Moments, and Hu's Moments were combined with the following classifiers: Bayes, k-Nearest Neighbor (kNN) with 1, 3, and 5 nearest neighbors, MultiLayer Perceptron (MLP) with two configurations, Support Vector Machines (SVM) with four different kernel types (linear, polynomial, radial basis function (RBF) and sigmoid). For all the using cases the method of partitioning data Hold Out. Measurements of precision, sensitivity, specificity and positive predictive values, as well as the confusion matrix were used to evaluate the classifiers. The SVM with polynomial using the GLCM extractor had the highest accuracy rate of 89.00%, specificity of 86.93%, sensitivity of 80.69% and positive predictive values of 80.34%. The time required for this combination, which was the best, was only 0.6ms. The results showed that this approach generated a new methodology for the analysis of non-grain oriented electrical steels.