Abstract
This paper introduces a computer vision engine for weld quality control of door panel manufacturing. The main algorithm carries out three tasks: object detection, image segmentation and image classification. These tasks are implemented by an ensemble of deep learning models, where each model fulfill a task. The overall performance of the computer vision system reaches an accuracy higher than 0.99 with a well suited computing time for the industrial process.
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Acknowledgements
This work has been founded by the grant: 10/18/BU/0030 Linea: planes estratégicos i+d. Título del proyecto: Despliegue de estrategia digital e industria 4.0 en grupo Antolin. Título del plan estratégico: Plan estratégico de innovaciín de Grupo Antolin 2022-24 expedientes contenidos en el plan estratégico.
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Moreno, R., María Sanjuán, J., Del Río Cristóbal, M., Shankar Muthuselvam, R., Wang, T. (2023). A Deep Learning Ensemble for Ultrasonic Weld Quality Control. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_14
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DOI: https://doi.org/10.1007/978-3-031-42536-3_14
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