Strong classification system for wear identification on milling processes using computer vision and ensemble learning
Metallic pieces are typically machined by continuous tool passes, which usually causes regular patterns in the form of straight edges in the surface of the pieces. An irregular pattern on the surface of the piece implies a decrease of the quality of the machined piece. In this paper, we propose an a...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Ajuntament de Barcelona |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/18274 |
| Acceso en línea: | https://hdl.handle.net/10612/18274 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingenierías classifier, machine learning clasificador |
| Sumario: | Metallic pieces are typically machined by continuous tool passes, which usually causes regular patterns in the form of straight edges in the surface of the pieces. An irregular pattern on the surface of the piece implies a decrease of the quality of the machined piece. In this paper, we propose an acquisition system and a machine-vision based method to describe the texture of the inner and outer surfaces of machined pieces with cylindrical holes. In order to capture images of the hole surface, we used a microscope camera connected to a rigid industrial boroscope. Considering the extracted texture descriptors, a significant correlation is shown. Consequently, the feature vector is reduced and then classified by several algorithms using an exhaustive grid search strategy with 10 fold cross validation. Best results are achieved with the Extremely Randomized Trees classifier with a mean test score on the hold out set of 92.98%, what improves previous research and meets the requirements of the field. |
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