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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4 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 OTIMIZAÇÃO DE PROCESSOS DE MANUFATURA ADITIVA POR MEIO DA MODELAGEM 3D E SIMULAÇÕES NA INDUSTRIA 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) DIAS, Jonathan Oliveira; VIEIRA JUNIOR, MiltonThe 4th industrial revolution, also known as Industry 4.0, characterized by the integration of digital technologies, has significantly transformed production processes. Among these innovations, additive manufacturing stands out as a strategic alternative to traditional subtractive methods, offering greater flexibility and efficiency in material usage. This study explores the optimization of additive manufacturing processes using 3D modeling and computer simulation in the context of Industry 4.0. Additive manufacturing, also known as 3D printing, enables the production of more complex parts at lower costs by eliminating inventory storage requirements and manufacturing steps. However, challenges such as quality control, scalability, and standardization still need to be addressed. The research employs CAD/CAE/CAM tools, 3D modeling, and finite element method (FEM) simulations to predict the structural behavior of parts and adjust parameters such as print orientation and fill patterns. The integration of Industry 4.0 technologies with digital tools can reduce costs, improve quality, and increase flexibility, meeting the demands of an increasingly competitive and sustainable market.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 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.