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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3 resultados
Resultados da Pesquisa
Item Implementação de um sistema de visão com deep learning para otimizar o processo de inspeção de emendas das bobinas na fabricação do cinturão de segurança(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOUZA, Kerlisson Silva de; SANTOS, Eliton Smith dosProduct quality is one of the primary criteria considered by customers when choosing an item. Additionally, it is an essential factor for companies to stand out in a highly competitive market. In the Manaus Industrial Hub (PIM), in a machine used for producing safety belts, defect detection is a crucial stage in the production process. To enhance this task, Artificial Intelligence (AI) was implemented, standing out for its high efficiency in analyzing and processing data in industrial environments. The data was captured in image format by a camera, and using Deep Learning (DL) techniques, an intelligent algorithm capable of detecting faults was developed. Due to its autonomous learning capability and ability to identify and characterize defects, this algorithm represents the future of automated inspection. It has already achieved significant success in applications such as object identification and classification, facial recognition, and fault diagnostics. Given this context, the aim of this study is to propose an ideal solution to minimize failures in the production process of safety belts. The proposal seeks to automate the currently manual step using the concept of computer vision with AI, ensuring greater efficiency and reliability in the production process. The research, development, and application of AI with the algorithm in the case study were conducted in the R&D laboratory of the company located in the Manaus Industrial Hub (PIM). The project utilized product inputs, a camera equipped with a lens for capturing images, and a computer for data storage and algorithm development. The application of AI in this environment uses computer vision systems to process image data. For this, a program was developed in Python with the PySimpleGUI library. The trained model was evaluated based on loss and accuracy metrics on the test set, achieving values of 0 and 100%, respectively. During testing, new belts were used, reaching 100% accuracy in the results. The proposed model showed excellent results. With data processed by AI using Deep Learning (DL) techniques, real-time inspection of the belts was achieved. Additionally, the network achieved perfect accuracy in all tests conducted on the belts, demonstrating the effectiveness of the solution.Item Aplicação de inferência fuzzy para tomada de decisão em processos de SMT: um caso no PIM(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) LEITE, Herbety Lima; ALENCAR, David Barbosa deSurface Mount Technology (SMT) is widely used in the production of electronic components, especially in the Manaus Industrial Pole (PIM), one of the largest electronics production centers in Brazil. However, its complexity and sensitivity to parameter variations, such as temperature and alignment, pose significant challenges to process accuracy and consistency. Traditional control methods, such as PID, often fail to meet the adaptability and efficiency demands required in these scenarios. Fuzzy logic emerges as a promising alternative, providing support in situations of uncertainty and variability. This study developed and implemented a fuzzy inference system for controlling critical parameters in SMT processes at PIM. The methodology involved identifying key variables, such as temperature and pressure, and constructing a fuzzy model based on linguistic rules and expert knowledge. The system was validated through comparative analysis before and after its implementation, evaluating aspects such as production efficiency, rework rate, and final product quality. The application of the fuzzy system resulted in significant improvements in the efficiency of the SMT process. There was a reduction in assembly failures, less need for rework, and greater consistency in production. In addition, the system demonstrated greater adaptability to operational variations compared to traditional control methods, contributing to more sustainable and efficient production. The use of fuzzy inference systems in SMT processes at PIM proved to be an effective approach for optimizing decision-making, improving quality and production efficiency. This research reinforces the role of fuzzy logic as a viable tool for meeting the demands of Industry 4.0, promoting innovations in industrial automation, and contributing to the competitiveness of PIM companies.Item Aplicação De Inferência Fuzzy Para Tomada De Decisão Em Processos De SMT No Polo Industrial De Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) LEITE, Herbety Lima; ALENCAR, David Barbosa De; ALVES, Hellen Lima Leite; SANTOS, Eliton Smith Dos; CAMPOS, Paola Souto; MORÃES, Nadime Mustafa; LEITE, Jandecy CabralSurface Mount Technology (SMT) is widely used in the production of electronic components, especially in the Manaus Industrial Pole (PIM), one of the largest electronics production centers in Brazil. However, its complexity and sensitivity to parameter variations, such as temperature and alignment, present significant challenges to the precision and consistency of the process. Traditional control methods, such as PID, often fail to meet the adaptability and efficiency demands required in these scenarios. Fuzzy logic emerges as a promising alternative, offering support in situations of uncertainty and variability. This study developed and implemented a fuzzy inference system for controlling critical parameters in SMT processes at PIM. The methodology involved identifying key variables, such as temperature and pressure, and building a fuzzy model based on linguistic rules and expert knowledge. The system was validated through a comparative analysis before and after its implementation, evaluating aspects such as production efficiency, rework rate, and final product quality. The application of the fuzzy system resulted in significant improvements in SMT process efficiency. A reduction in assembly failures, less need for rework, and greater production consistency were observed. In addition, the system demonstrated greater adaptability to operational variations compared to traditional control methods, contributing to more sustainable and efficient production. The use of fuzzy inference systems in SMT processes at PIM proved to be an effective approach to optimize decision-making, improving quality and production efficiency. This research reinforces the role of fuzzy logic as a viable tool to meet the demands of Industry 4.0, promoting innovations in industrial automation and contributing to the competitiveness of PIM companies.