New approach to evaluate a non-grain oriented electrical steel electromagnetic performance using photomicrographic analysis via digital image processing

Abstract

The growing global demand for energy makes it necessary to adopt measures ranging from the exploration of new energy sources to the development of technology for machinery and equipment with greater energy efficiency. Non-grain oriented electrical steels are widely used in the construction of rotors and stators that form the core of electric motors, and their microstructures are directly related to its electromagnetic performance. This paper presents a new, fast and efficient method for the classification of non-grain oriented electrical steel microstructural states and their electromagnetic performance using photomicrographic analysis. The study was performed on non-grain oriented electrical steel samples with 1.28% silicon, cold-rolled with reductions of 50% and 70%, annealed in box at 730 °C for 12 h, and subjected to a subsequent annealing heat treatment for grain growth at 620 °C, 730 °C, 840 °C and 900 °C for 1, 10, 100 and 1000 min at each temperature. A total of 32 samples were used to form a database with 192 images. Our approach used a combination of extractor features (GLCM, LBP and moments) with the classifiers (Bayes, K-NN, K-means, MLP and SVM), also combined with two data partitioning, and the hold out and leave one out. KNN with 1 neighbor using the GLCM extractor showed the highest accuracy rate of 97.44%, and values greater than 96.0% for the other validation methods. The time required for the test was only 15.4 ms. The results obtained with this proposed approach, generate a new approach to evaluate a non-grain oriented electrical steel electromagnetic performance. Keywords: Electrical steel, Electromagnetic performance, Digital image processing, Pattern recognition