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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9 resultados
Resultados da Pesquisa
Item Sinergia entre Modelagem 3D e Redes Neurais Artificiais na Otimização da Manufatura Aditiva no Contexto da Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) DIAS, Jonathan Oliveira; ALENCAR, David Barbosa deAdditive manufacturing (AM), widely known as 3D printing, emerges as one of the key technologies of Industry 4.0, enabling the production of parts with high geometric complexity and customization. This study investigates how the integration between 3D modeling and Artificial Neural Networks (ANNs) enhances the efficiency and quality of AM processes. 3D modeling supports detailed simulations of material behavior and manufacturing processes, while ANNs provide predictive analysis and learning from large data volumes, allowing automatic and dynamic adjustments to parameters such as speed, temperature, and fill patterns. The results show significant improvements in reliability, waste reduction, and energy consumption, aligning production with sustainability demands.Item Optimization of the Manufacturing Process of Cardboard Packaging with Fuzzy Logic: Case Study Company in the Industrial Polo of Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) FERNANDES, Everson Lima ALENCAR, David Barbosa de SANCHES, Antônio Estanislau PINTO JÚNIOR, José Roberto Lira; LEITE, Jandecy CabralThis study arose from the need to optimize the manufacturing of cardboard packaging for LCD monitors, reducing assembly complexity and the number of box folds without compromising dimensional requirements, mechanical strength, and customer acceptance criteria. The research used exploratory and descriptive methods, applying fuzzy logic to validate the new box layouts. The production process was analyzed to identify improvements in packaging design, ensuring greater handling efficiency and cost reduction. The results indicate that the proposed optimization significantly reduced assembly time and waste, contributing to production line efficiency.Item Automation of Tambaqui Fish Pond Aeration for Energy Efficiency in the Brazilian Amazon(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) BINATTI, Fabio Cavalcante; OLIVEIRA, Edson Farias deThe increase in the demand for fish highlights the aquaculture potential of the Brazilian Amazon. The high cost of electric energy, lack of technical staff, and the absence of continuous control of water quality parameters are some of the factors that hinder the growth of this activity. This study evaluated two opportunities for automation and optimization in commercial tambaqui farms in ponds in Amazonas, Brazil. The first is the manual control and recording of physical-chemical properties of the water, and the second is the high consumption of electricity by the aerators. Thus, we developed continuous monitoring of water quality parameters through automatic gauging, using a floating platform with onboard sensors, called the Autonomous Experimental Station (AES), which controls the startup and shutdown of the aerators according to established parameters, providing the oxygen necessary for maintaining fish life, eliminating electricity waste, and recording the evaluated parameters. As a result, after integrating the AES in excavated tanks, it was possible to reduce 26% in electric energy consumption for adult fish and 52% for juvenile fish, besides registering the values of DO, pH, and temperature, generating savings for the producer and contributing to the sustainability of the activity in the Amazon.Item Integration of 3D Modeling, Simulations and Artificial Neural Networks in the Optimization of Additive Manufacturing Processes with the Maturity of Industry 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) DIAS, Jonathan Oliveira; VIEIRA JUNIOR, Milton; LEITE, Jandecy Cabral; FERREIRA, Genilson Roberto Maciel; VIEIRA JUNIOR, MiltonAdditive manufacturing (AM), commonly known as 3D printing, is one of the main technologies of Industry 4.0, enabling the production of parts with high geometric complexity and customization. The integration of 3D modeling and Artificial Neural Networks (ANNs) has proven essential for improving the performance and efficiency of AM processes. This paper explores how this integration contributes to optimizing critical parameters, reducing waste, and increasing product quality. 3D modeling serves as the foundation for accurate simulations of material behavior and manufacturing processes, while ANNs provide predictive analysis, learning from large datasets to identify patterns and forecast outcomes. The technological maturity of Industry 4.0 further drives this integration, utilizing advanced IoT tools, cloud computing, and big data. Challenges include the need for advanced technological infrastructure, skilled labor, and robust algorithm development for ANNs. However, the benefits outweigh the challenges, bringing significant advances in production flexibility and customization. We conclude that combining 3D modeling and ANNs in additive manufacturing represents a milestone in Industry 4.0’s evolution, promoting technological innovation and operational efficiency.Item Análise decisória para implementação de estratégias integradas para a redução de setup em processos industriais utilizando lógica fuzzy(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOUZA, Vanessa Silva; ALENCAR, David Barbosa de; SILVA, Ítalo Rodrigo Soares; LEITE, Jandecy CabralThis article explores the application of Fuzzy Logic as a decision support tool for implementing integrated strategies aimed at reducing setup times in industrial processes. The study was conducted in a company in the Manaus Industrial Park, in the electronics sector, and developed a computational model based on Fuzzy Logic to analyze variables affecting setup time and production efficiency. The results showed that the combination of Tools and Equipment with Advanced Planning has a significant impact of 86% on setup time, highlighting the importance of effective management of these variables to reduce bottlenecks and time waste. Palavras-chave: Lógica Fuzzy, Estratégias de Redução do Tempo de Setup, Eficiência Operacional, Otimização de Processos, ProdutividadeItem Otimização de Processos Industriais no Polo Industrial de Manaus (PIM): Desenvolvimento de um Dispositivo para Eliminar Contato Manual, Reduzir Esforço Repetitivo e Riscos Ergonômicos, Melhorando a Qualidade e Reduzindo Custos(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) CRUZ, Cristovão Santiago da; GUIMARÃES, Gil Eduardo; MARINELLI FILHO, Nelson; CORREA, Geraldo Nunes; TREVISOL, Janyel.; LEITE, Jandecy CabralThis study presents the development and implementation of an automated device for assembling steel balls and applying grease to column tubes in motorcycle production lines. The objective was to eliminate manual contact, promote ergonomic improvements, and optimize the production cycle. The methodology included requirements analysis, prototyping, pilot testing, and validation in a real environment. The results demonstrated a 25% reduction in cycle time, a 40% increase in product quality, and significant improvements in ergonomic indices. The economic analysis showed a financial return in less than 12 months. This work highlights the benefits of automation and suggests expanding the device to other stages of the production process, integrating advanced technologies such as artificial intelligence and cyber-physical systems to enhance efficiency and quality gains.Item Sinergia entre modelagem 3D e redes neurais artificiais na otimização da manufatura aditiva no contexto da Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) DIAS, Jonathan Oliveira; VIEIRA JUNIOR, Milton; LEITE, Jandecy Cabral; FERREIRA, Genilson Roberto Maciel; LEITE, Jandecy CabralAdditive manufacturing (AM), widely known as 3D printing, emerges as one of the fundamental technologies of Industry 4.0, enabling the fabrication of highly complex and customized parts. This study investigates how the integration between 3D modeling and Artificial Neural Networks (ANNs) enhances the efficiency and quality of AM processes. 3D modeling provides support for detailed simulations of material behavior and manufacturing processes, while ANNs offer predictive analysis and learning from large volumes of data, allowing automatic and dynamic adjustments in parameters such as speed, temperature, and filling patterns. The results demonstrate significant improvements in reliability, waste reduction, and energy consumption, aligning production with sustainability demands. Additionally, the maturity level of Industry 4.0 contributes to this integration, with the use of tools such as IoT, cloud computing, and big data, creating an intelligent and connected production environment. Despite challenges related to technological infrastructure, workforce qualification, and ANN algorithm development, the benefits outweigh the obstacles, resulting in greater flexibility and customization of production processes. This study concludes that the integration of 3D modeling and ANNs in additive manufacturing represents a milestone in digital transformation and industrial sector competitiveness, standing out as a promising approach for process optimization and data-driven decision-making.Item Análise decisória para implementação de estratégias integradas para a redução de setup em processos industriais utilizando lógica fuzzy(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-06-09) SOUZA, Vanessa Silva; ALENCAR, David Barbosa de; SILVA, Ítalo Rodrigo SoaresThis article explores the application of Fuzzy Logic as a decision support tool for implementing integrated strategies aimed at reducing setup times in industrial processes. The study seeks to optimize operational efficiency and reduce variability in production processes, ultimately enhancing productivity. A computational model based on Fuzzy Logic was developed to analyze key variables impacting setup times and associated waste. The research was conducted in a company in the Manaus Industrial Park, specializing in electronics. The results showed that the combination of Tools and Equipment with Advanced Planning variables significantly impacts setup time by 86%, highlighting the importance of managing these variables to improve productivity.Item Otimização do Processo de Inserção Manual de Bobinadeira de Motores Elétricos Utilizando Técnicas do Lean Manufacturing(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023-10-12) SOUZA, Rene Brito de; LEITE, Jandecy CabralThis study addresses the optimization of the manual coil insertion process in electric motors using Lean Manufacturing techniques. The main objective is to improve the efficiency and productivity of the coil winding sector in an electric motor factory. The research employed methodologies such as World Class Manufacturing (WCM), Spaghetti Diagram, Mura, Muri, Muda, and Time Study. Lean tools were implemented to reorganize workflow and redistribute tasks, resulting in a more balanced workload and a significant reduction in productivity losses. This study demonstrates the feasibility of applying Lean techniques to industrial processes and the importance of continuous adaptation to achieve operational excellence.