Abstract
Fulfilment of quality standards in manufacturing processes is an essential task and often increases production costs. Specifically, the appropriate edge finishing of machine workpieces is one of the requirements so as to avoid the presence of burrs. In this paper, a vision-based system that employs contour features is proposed to detect and classify images of edge workpieces. In the first stage, we locate the region of the image that contains the edge of the part and in the second one, more precised operations provide detailed information in order to detect the edge type of the machined part. Calculated feature vector feeds supervised classifiers to determine the best approach to this dataset. Random Forest Classifier yields the best results obtaining a 90% of precision, recall and F1-score in the test dataset, which satisfies the experts demand to these processes.
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Acknowledgements
We gratefully acknowledge the financial support of Spanish Ministry of Economy, Industry and Competitiveness, through grant PID2019-108277GB-C21. Virginia Riego would like to thank Universidad de León for its funding support for her doctoral studies. Claudia Álvarez would like to thank the regional Government of Castilla y León for its funding support under the grant BDNS (487971).
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Riego del Castillo, V., Sánchez-González, L., Álvarez-Aparicio, C. (2021). Classification of Burrs Using Contour Features of Image in Milling Workpieces. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_18
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