PPG.EGPSA/ITEGAM

URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1

A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.

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Filtros

2022 - 20243

Configurações

Tem arquivos: SimAssunto: search.filters.subject.Manufatura Inteligente

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
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    Indústria 4.0: uma proposta para implementação em uma empresa metal mecânica no Polo Industrial de Manaus
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) PEREIRA, Danilo Serrão; GUIMARÃES, Gil Eduardo
    This work presents a proposal for the implementation of Industry 4.0 technologies in a mechanical metal company located in the Industrial Pole of Manaus (PIM). The research addresses the company's maturity level regarding Industry 4.0 using the Impuls questionnaire as the main tool. Through detailed analysis, the key gaps and opportunities for the company's digital transformation are identified, focusing on areas such as automation, data analytics, and system integration. The study proposes a roadmap for the implementation of Industry 4.0 technologies, with specific steps for automating production processes and workforce training. Additionally, challenges such as resistance to change and the shortage of qualified labor are discussed, along with strategies to overcome them. The research concludes that transitioning to Industry 4.0 is essential to enhance the company's competitiveness, promote sustainability, and improve operational efficiency.
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    Digital Technologies Review for Manufacturing Processes
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) PARENTE, Ricardo Silva SILVA, Italo Rodrigo Soares SIQUEIRA JUNIOR, Paulo Oliveira UHLMANN, Iracyanne Retto; LEITE, Jandecy Cabral
    The industrial processes transformation caused by Industry 4.0 is advancing in countries like China, Japan, Germany, and the United States. However, developing countries such as Brazil still face challenges in adapting to the digital era. This study presents a review of the main technologies used in smart manufacturing and the challenges of its implementation in Brazil. The research was based on 114 articles and two books collected from the Web of Science database. The results indicate that the primary challenges for Industry 4.0 adoption in Brazil include poor technological infrastructure, lack of investment in technology, and insufficient training of qualified professionals. Although the study focuses on the Brazilian scenario, its conclusions are applicable to other emerging countries, providing a general overview of concepts and practical applications developed by the international academic community.
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    Application of Deep Neural Network in Intelligent System with Production Dashboard
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) RAMOS JUNIOR, Juarez da Silva LEITE, Jandecy Cabral GOMES, Marivan Silva PAULA, Railma Lima de CARVALHO, Michael da Silva SILVA, Ítalo Rodrigo Soares SIQUEIRA JUNIOR, Paulo Oliveira PARENTE, Ricardo Silva MIRANDA, Luís Gabryel dos Santos; LEITE, Jandecy Cabral
    The Lean Manufacturing process is a strategic methodology aimed at reducing production waste, ensuring product quality, minimizing delivery time, and reducing defects. However, the company analyzed lacks technologies that enable the effective implementation of this methodology. This study proposes the development of an Intelligent Lean Manufacturing System based on the requirements of a Manufacturing Execution System (MES), assisting decision-making in production control and management through technologies such as Artificial Intelligence, Internet of Things (IoT), and Embedded Systems. The system includes Deep Neural Network (DNN) algorithms to forecast demand, optimize processes, and monitor real-time indicators. The research demonstrates that the application of these technologies can reduce costs and improve production quality, raising manufacturing maturity levels to Industry 4.0 standards.