Abstract
Milling workpiece present a regular pattern when they are correctly machined. However, if some problems occur, the pattern is not so homogeneous and, consequently, its quality is reduced. This paper proposes a method based on the use of texture descriptors in order to detect workpiece wear in milling automatically. Images are captured by using a boroscope connected to a camera and the whole inner surface of the workpiece is analysed. Then texture features are computed from the coocurrence for each image. Next, feature vectors are classified by 4 different approaches, Decision Trees, K Neighbors, Naïve Bayes and a Multilayer Perceptron. Linear discriminant analysis reduces the number of features from 6 to 2 without loosing accuracy. A hit rate of 91.8% is achieved with Decision Trees what fulfils the industrial requirements.
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We gratefully acknowledge the financial support of Spanish Ministry of Economy, Industry and Competitiveness, through grant DPI2016-79960-C3-2-P.
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Castejón-Limas, M., Sánchez-González, L., Díez-González, J., Fernández-Robles, L., Riego, V., Pérez, H. (2019). Texture Descriptors for Automatic Estimation of Workpiece Quality in Milling. 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_62
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