Abstract
There are several industries that supplies key elements to other industries where they are critical. Hence, foundry castings are subject to very strict safety controls to assure the quality of the manufactured castings. In the last years, the use of computer vision technologies to control the surface quality. In particular, we have focused our work on inclusions, cold laps and misruns. We propose a new methodology that detects and categorises imperfections on the surface. To this end, we compared several features extracted from the images to highlight the regions of the casting that may be affected and, then, we applied several machine-learning techniques to classify the regions. Despite Deep Learning techniques have a very good performance in this problems, they need a huge dataset to get this results. In this case, due to the size of the dataset (which is a real problem in a real environment), we have use traditional machine learning techniques. Our experiments shows that this method obtains high precision rates, in general, and our best results are a 96,64% of accuracy and 0.9763 of area under ROC curve.
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Pastor-López, I., Sanz, B., de la Puerta, J.G., Bringas, P.G. (2019). Surface Defect Modelling Using Co-occurrence Matrix and Fast Fourier Transformation. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_63
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