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

2023 - 20244

Configurações

Assunto: search.filters.subject.Indústria 4.0

Resultados da Pesquisa

Agora exibindo 1 - 4 de 4
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    Smart Gateway 4.0: Prototipagem de dispositivo inteligente para controladoras de fator de potência como estratégia para avanço da maturidade digital através de IoT e Edge Computing em sistemas de gestão de energia
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SERRÃO, Alacy da Conceição da Silva; LEITE, Jandecy Cabral
    The advancement of Industry 4.0 technologies and the demand for energy efficiency require innovative solutions in electric energy management. This dissertation develops the SMART GATEWAY 4.0, an intelligent gateway prototype for power factor controllers, using IoT and Edge Computing. The goal is to optimize the commissioning and management of these controllers and elevate the digital maturity level of industries according to the ACATECH Industry 4.0 Maturity Index. Communication was carried out using Modbus, HTTP, and REST API protocols. Results demonstrated that SMART GATEWAY 4.0 significantly improves energy management efficiency, fostering the digitalization and automation of energy-related industrial processes.
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    Development of a Prototype Gateway for Data Collection and Transmission in Material Handling Systems - Gate Move 4.0
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-06-09) AMARAL, Carlos Henrique; LEITE, Jandecy Cabral; RIBEIRO, Paulo Francisco da Silva; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; DIRANE, Eduardo Nunes; MENDONÇA, Pedro Henrique Barros
    The article discusses the development of a gateway prototype named Gate Move 4.0, designed for data collection and transmission in material handling systems. The goal of the study is to improve operational efficiency and decision-making in industries, especially with the advent of Industry 4.0. The prototype was evaluated under various operating conditions, demonstrating efficiency in data collection, connectivity, and performance in adverse environments. The methodology included requirements analysis and the implementation of sensor technologies and communication protocols. Results showed that Gate Move 4.0 is a viable solution for material handling systems.
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    Developing An AMR Prototype With Processing Offloading Using 5G Servers For Industry 4.0
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-09-01) COSTA, Gledyson Cidade da; RODRÍGUEZ, Carlos Manoel Taboada; LEITE, Jandecy Cabral
    This study explores the development of an autonomous mobile robot (AMR) prototype with processing offloading using 5G servers for Industry 4.0. The goal is to improve AMR performance by reducing the computational load on the robot and optimizing energy efficiency. The developed architecture integrates the AMR with the 5G infrastructure, distributing processing between the robot and external servers, enabling real-time response. The results show that this approach improves AMR navigation and efficiency in industrial environments.
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    Desenvolvimento de sistema inteligente de manufatura enxuta para gerenciamento e controle da produção do medidor eletrônico.
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2023-10-20) RAMOS JÚNIOR, Juarez da Silva; LEITE, Jandecy Cabral; http://lattes.cnpq.br/7279183940171317
    This project presents a viable solution to the problem of communication, integration and control of information in the manufacturing process of the electronic meter, a product that includes an Intelligent System. The aim was to develop an Intelligent System for Lean Manufacturing to support decision-making when using Production Control processes to guarantee product quality and reduce production costs. The advancement of technologies in manufacturing processes aligned with methodologies that enable production efficiency, this project demonstrates a focus on the modernization of factories by using the pillars of industry 4.0 to enable new investment perspectives and fit the company into the process maturity requirements. The software was developed for a company in the Manaus Industrial Estate (PIM). It used Machine Learning, Deep Learning and various methods such as: optimization, bio-inspired, classification and pattern recognition, heuristic, iterative and Newton methods. The results showed that the main development phases were validated, obtaining a computational model applied to production demand forecasting with knowledge generation made available on production and quality dashboards and the Intelligent System with integration via API and development stacks with modern resources (web frameworks), developing with quality metrics: reliability, security, availability and robustness.