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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.

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
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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

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