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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/66738

    Título
    3DWS: reliable segmentation on intelligent welding systems with 3D convolutions
    Autor
    Fernández Iglesias, Jesús
    Valerieva Ivanova, Dilyana
    Higuero, Luis
    Sahelices Fernández, BenjamínAutoridad UVA Orcid
    Año del Documento
    2023
    Editorial
    Springer
    Descripción
    Producción Científica
    Documento Fuente
    Journal of Intelligent Manufacturing, 2023.
    Abstract
    Automated industrial welding processes depend on a large number of factors interacting with high complexity resulting in some sporadic and random variability of the manufactured product that may affect its quality. It is therefore very important to have an accurate and stable quality control. In this work, a deep learning (DL) model is developed for semantic segmentation of weld seams using 3D stereo images of the seam. The objective is to correctly identify the shape and volume of the weld seam as this is the basic problem of quality control. To achieve this, a model called UNet++ has been developed, based on the UNet and UNet++ architectures, with a more complex topology and a simple encoder to achieve a good adaptation to the specific characteristics of the 3D data. The proposed model receives as input a voxelized 3D point cloud of the freshly welded part where noise is abundantly visible, and generates as output another 3D voxel grid where each voxel is semantically labeled. The experiments performed with parts built by a real weld line show a correct identification of the weld seams, obtaining values between 0.935 and 0.941 for the Dice Similarity Coefficient (DSC). As far as the authors are aware, this is the first 3D analysis proposal capable of generating shape and volume information of weld seams with almost perfect noise filtering.
    Materias Unesco
    33 Ciencias Tecnológicas
    Palabras Clave
    Automated manufacturing
    Deep learning
    3D convolutions
    Semantic segmentation
    Industry 4.0
    ISSN
    0956-5515
    Revisión por pares
    SI
    DOI
    10.1007/s10845-023-02230-0
    Patrocinador
    Junta de Castilla y León y FEDER (programa “Subvenciones para la realización de proyectos de I+D+i en el ámbito de Castilla y León cofinanciados con FEDER” bajo Convenio de Donación No. FUNGE 061-217731)
    Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE
    Version del Editor
    https://link.springer.com/article/10.1007/s10845-023-02230-0
    Propietario de los Derechos
    © 2023 The Author(s)
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/66738
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • GCME - Artículos de revista [57]
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