Dezembro 2020 vol. 7 num. 2 - VI Simpósio Internacional de Inovação e Tecnologia

Artigo completo - Open Access.

Idioma principal | Segundo idioma

DIRT IN AUTOMOTIVE PAINTING: AN APPROACH SUPPORTED BYCYBER-PHYSICAL SYSTEMS FOR PROCESS AUTOMATION

SUJIDADE NA PINTURA AUTOMOTIVA: UMA ABORDAGEM APOIADA POR SISTEMAS CIBERFÍSICOS PARA AUTOMAÇÃO DO PROCESSO

Fernandes, Juliana a ; Lepikson, Herman a ; Leite, Cristiane b . ;

Artigo completo:

Dirt in vehicle paint impacts the final quality and appearance even when itis small. Controlling dirt in painting systems requires considerable work due to thenumber of variables that can generate defects. In the automotive industry, a dirtspecialist is responsible for collecting dirt on vehicles, analyzing, identifying anddirecting actions for process control. As there is dirt which is similar but not the same,the process requires the great experience of an expert. The goal of this paper is toidentify technologies and appropriated neural networking as a solution path to supportthe development of a digital approach to automate the painting quality controlimproving decision-making. Research has shown many available technologies,however with limitations to implement.

Artigo completo:

Sujidade na pintura afeta a qualidade final do veículo, mesmo quando pequena. Controlar a sujeira na pintura exige forte trabalho devido ao número de variáveis que geram defeito. Nesse cenário, o especialista de sujidade é responsável por coletar sujidade no veículo, analisar e direcionar ações para controle de processos. Como existem sujeiras semelhantes, ter experiência é necessário para definir assertivamente o tipo de sujidade. O objetivo deste artigo é, a título de estado da arte, identificar tecnologias e rede neural apropriada como caminho de solução para apoiar o desenvolvimento de uma abordagem para automatizar e melhorar a tomada de decisão no processo de identificação de sujidade. Pesquisas mostraram tecnologias disponíveis, mas que apresentam limitações para sua implementação.

Download (PDF)

Palavras-chave: Dirt in paint; Paint defect; Automation on dirt analysis; Image classification; Convolutional neural networks,

Palavras-chave: Sujidade em Pintura automotiva; Defeito de pintura; Automação emanálise de sujidade; Classificação de Imagem; Rede neural convolucional;,

DOI: 10.5151/siintec2020-DIRTIN

Referências bibliográficas
  • [1] JU, Feng, et al. Modeling Quality Propagation in Automotive Paint Shops: An Application Study. IFAC Proceedings Volumes, p. 1890-1895, 2013. [2] GEFFEN, Charlette A.; ROTHENBERG, Sandra. Suppliers and environmental innovation. International Journal of Operations & Production Management, 2000. [3] BYSKO, Sara; KRYSTEK, Jolanta; BYSKO, Szymon. Automotive Paint Shop 4.0. Computers & Industrial Engineering, p. 105546, 2018. [4] YAO, Yuan, et al. Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network. Applied Ocean Research, p. 101823, 2019. [5] Available at: . Acessed at: July 21st. 2020. VI INTERNATIONAL SYMPOSIUM ON INNOVATION AND TECHNOLOGY (SIINTEC) "Challenges in science, technology and innovation after COVID-19" ISSN: 2357-7592 [6] WOLFF, Ulrich; THOMAS, Hartmut; OSTERHOLD, Michael. Analysis of paint defects by mass spectroscopy (LAMMA®/ToF-SIMS). Progress in organic coatings, p. 163-171, 2004. [7] KIESELBACH, Kim Katharina; NÖTHEN, Matthias; HEUER, Henning. Development of a visual inspection system and the corresponding algorithm for the detection and subsequent classification of paint defects on car bodies in the automotive industry. Journal of Coatings Technology and Research, v. 16, n. 4, p. 1033-1042, 2019. [8] ZHANG, Jing et al. Defect inspection of coated automobile roofs using a single camera. IEEJ Transactions on Electrical and Electronic Engineering, v. 15, n. 4, p. 616-625, 2020. [9] GENG, Lei et al. Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), v. 53, n. 1, p. 128-144, 2019. [10] ZHOU, Qinbang et al. An automatic surface defect inspection system for automobiles using machine vision methods. Sensors, v. 19, n. 3, p. 644, 2019. [11] CAO, Xiaohui et al. Defect detection method for rail surface based on linestructured light. Measurement, p. 107771, 2020. [12] ZARIE, M.; JAHEDSARAVANI, A.; MASSINAEI, M. Flotation froth image classification using convolutional neural networks. Minerals Engineering, v. 155, p. 106443, 2020. [13] PARVATHY, Velmurugan Subbiah; POTHIRAJ, Sivakumar; SAMPSON, Jenyfal. Optimal Deep Neural Network model based multimodality fused medical image classification. Physical Communication, p. 101119, 2020. [14] RAI, Amit Kumar et al. Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network. Procedia Computer Science, v. 167, p. 987-993, 2020. [15] ZHANG, Bin; ZHAO, Lin; ZHANG, Xiaoli. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. Remote Sensing of Environment, v. 247, p. 111938, 2020. [16] FANG, Leyuan et al. Iterative fusion convolutional neural networks for classification of optical coherence tomography images. Journal of Visual Communication and Image Representation, v. 59, p. 327-333, 2019. [17] MOLINA, Jaime et al. On the detection of defects on specular car body surfaces. Robotics and Computer-Integrated Manufacturing, v. 48, p. 263-278, 2017. [18] TORNERO MONTSERRAT, Josep et al. Detección de defectos en carrocerías de vehículos basado en visión artificial: Diseño e implantación. Revista Iberoamericana de Automática e Informática Industrial (RIAI), v. 9, n. 1, p. 93-104, 2012. [19] Available at: . Acessed at: July 21st. 2020.
Como citar:

Fernandes, Juliana a; Lepikson, Herman a; Leite, Cristiane b .; "DIRT IN AUTOMOTIVE PAINTING: AN APPROACH SUPPORTED BYCYBER-PHYSICAL SYSTEMS FOR PROCESS AUTOMATION", p. 551-557 . In: Anais do VI Simpósio Internacional de Inovação e Tecnologia. São Paulo: Blucher, 2020.
ISSN 2357-7592, ISBN: 2357-7592
DOI 10.5151/siintec2020-DIRTIN

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações