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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Assunto: search.filters.subject.Eficiência operacional

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Melhoria de desempenho em equipamentos de manufatura: um estudo de caso sobre a redução de queimas de placas eletrônicas em máquinas Okuma
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SANTOS, James Silva dos; BRITO JUNIOR, Jorge de Almeida
    This study addresses the performance improvement of Computer Numerical Control Okuma machines at Musashi da Amazônia Ltda., focusing on reducing failures in electronic boards caused by electromagnetic interference in the manufacturing environment. The primary objective was to decrease the incidence of these failures, enhance operational efficiency, and reduce maintenance costs. The methodology involved analyzing historical data, identifying interference sources, and installing electromagnetic interference filters. As a result, the filters significantly reduced failures, ensuring greater operational stability and lower costs. This work highlights the importance of power quality management for industrial efficiency.
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    Monitoramento dinâmico com Real Time Process Control, uma integração da tecnologia da Indústria 4.0 nos processos de fabricação industrial
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOUSA, Marcio Rizonildo Aquino de; ALENCAR, David Barbosa de
    The increasing complexity of manufacturing processes in the Industry 4.0 era presents significant challenges, particularly in the real-time verification and diagnosis of machine states. Currently, test stations are limited to displaying the pass or fail status of the cell under test, requiring multiple manual interactions with various tools to identify and resolve issues. This process is time-consuming, inefficient, and prone to errors, negatively impacting operational efficiency and product quality. This dissertation aims to investigate and present the implementation of Real Time Process Control (RTPC) as a solution to these challenges. The proposal involves integrating Industry 4.0 technologies such as the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and Cyber-Physical Systems (CPS) to develop a dynamic monitoring system that allows for immediate and accurate problem identification and resolution. The methods employed include analyzing log results from generated files, calculating Yield and production, and presenting data through an intuitive interface. The research utilized a detailed framework for the implementation of RTPC. The results obtained demonstrate that the implementation of RTPC significantly increased operational efficiency, reduced maintenance costs, and improved product quality, showing reductions in downtime and improvements in productivity, validating the effectiveness of this approach in optimizing industrial manufacturing processes.