MDL+ a manufacturing description language to describe and control assembling tasks in industry 4.0

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Título: MDL+ a manufacturing description language to describe and control assembling tasks in industry 4.0
Autor/es: Zamora Hernández, Mauricio Andrés | Chaves Ceciliano, Jose Andrez | Villalobos Granados, Alonso | Castro-Vargas, John Alejandro | Garcia-Rodriguez, Jose | Azorin-Lopez, Jorge
Grupo/s de investigación o GITE: Arquitecturas Inteligentes Aplicadas (AIA)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Palabras clave: Assembly representation | Automatic inspection | Control language | Industry 4.0 | Recommendation system
Área/s de conocimiento: Arquitectura y Tecnología de Computadores
Fecha de publicación: 25-feb-2022
Editor: Oxford University Press
Cita bibliográfica: Logic Journal of the IGPL. 2023, 31(4): 664-687. https://doi.org/10.1093/jigpal/jzac032
Resumen: The assembly of products or components by operators in industries is a complex task with recurring problems. In these processes, operators often make errors that can lead to defective products. Therefore, they need to be inspected later to verify their correct assembly. The main problems are caused by several reasons including high employee turnover, lack of experience in manufacturing specific products or confusion in interpreting instructions to assemble similar components. In this paper, a novel structured language aimed to describe the required actions to manufacture a product in industrial assembly environments is presented. The main contribution is to provide a formal language to feed automatic systems that can verify through visual control whether the actions performed by the operator are carried out following the standard described by this language. In general, the use of this formal language will allow to minimize the negative impact of errors during assembly and to reduce waste in many forms in the industry.
Patrocinador/es: “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00) and Tech4D-Predict project (grant “PID2020-119144RB-I00). This work has also been supported by the University of Alicante grant for PhD studies UAFPU2019-13.
URI: http://hdl.handle.net/10045/121916
ISSN: 1367-0751 (Print) | 1368-9894 (Online)
DOI: 10.1093/jigpal/jzac032
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © The Author(s) 2022. Published by Oxford University Press
Revisión científica: si
Versión del editor: https://doi.org/10.1093/jigpal/jzac032
Aparece en las colecciones:INV - AIA - Artículos de Revistas

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