Abstract
The surface of machined parts is one of the most scrutinised criteria, since it determines their machinability. In this paper, texture descriptors obtained from the Grey Level Co-ocurrence Matrix (GCLM) are used to detect and classify six different types of surface defects of hot-rolled steel strip collected in the NEU dataset. Then, the texture feature vectors are passed to multiple machine learning classifiers to determine the most appropriated one for the dataset, and it is found to be Random Forest. As the features are calculated considering multiple angles, a dimensionality reduction is developed to achieve 94.41% accuracy.
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Acknowledgement
We gratefully acknowledge the financial support of Spanish Ministry of Science and Innovation, through grant PID2019-108277GB-C21. Virginia Riego would like to thank Universidad de León for its funding support for her doctoral studies. Alexis Gutiérrez acknowledges a FPU fellowship provided by the Ministerio de Educación, Cultura y Deporte of Spain.
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del Castillo, V.R., Sánchez-González, L., Gutiérrez-Fernández, A. (2022). Texture Descriptors for Automatic Classification of Surface Defects of the Hot-Rolled Steel Strip. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_24
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