Artigos
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/5
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9 resultados
Resultados da Pesquisa
Item Automation and Intelligent Control in Drying and Curing of Paints and Varnishes: Application of Industry 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SOUZA, Raimundo Alberto Farias de; SANTOS, Eliton Smith dos; ALENCAR, David Barbosa de; CAMPOS, Paola Souto; MORAES, Nadine Mustafa; Jandecy Cabral LeiteThis research aims to investigate the technologies, methods, and challenges in the drying and curing process of paints and varnishes applied to reflective strips, focusing on implementing Industry 4.0-based solutions. It proposes an integrated hardware and software model to automatically detect the curing level through light radiation, along with a real-time control system to optimize the process. Among the evaluated technologies, ultraviolet light curing (photopolymerization) stands out, aiming to enhance industrial production with quality and efficiency.Item Development of Intelligent Devices for Communication and Data Pre-Processing in the Process of Electronic Meter Testing(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) RAMOS JUNIOR, Juarez da Silva; LEITE, Jandecy Cabral; GOMES, Marivan Silva; PAULA, Railma Lima de; CARVALHO, Michael da Silva; SILVA, Ítalo Rodrigo Soares; SIQUEIRA JUNIOR, Paulo Oliveira; PARENTE, Ricardo Silva; MIRANDA, Luís Gabryel dos Santos; LEITE, Jandecy CabralThis study investigates the development of intelligent devices for communication and data pre-processing in electronic meter testing. The research addresses the lack of efficient communication between electronic devices and the need for information synchronization to support decision-making in Industry 4.0. The article presents the development of a Lean Manufacturing Intelligent System, integrating API and machine learning algorithms for demand forecasting and process optimization. The results include embedded firmware for data collection, forecasting algorithms, and an interactive dashboard for production monitoring. The proposed solution aims to reduce costs and improve product quality.Item Application of Deep Neural Network in Intelligent System with Production Dashboard(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) RAMOS JUNIOR, Juarez da Silva; LEITE, Jandecy Cabral; GOMES, Marivan Silva; PAULA, Railma Lima de; CARVALHO, Michael da Silva; SILVA, Ítalo Rodrigo Soares; SIQUEIRA JUNIOR, Paulo Oliveira; PARENTE, Ricardo Silva; MIRANDA, Luís Gabryel dos Santos; LEITE, Jandecy CabralLean Manufacturing is a strategic methodology aimed at reducing production waste, ensuring product quality, and optimizing delivery times. However, many electronic meter manufacturing companies lack the necessary technologies to effectively implement this methodology. This study proposes the development of an Intelligent Lean Manufacturing System based on the requirements of a Manufacturing Execution System (MES), utilizing technologies such as Artificial Intelligence, the Internet of Things, and Embedded Systems. The system enables real-time decision-making for production control and management, reducing costs and improving product quality.Item Application of the NARX Model for Forecasting Wind Speed for Wind Energy Generation(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) PARENTE, Ricardo Silva ALENCAR, David Barbosa de SIQUEIRA JUNIOR, Paulo Oliveira SILVA, Ítalo Rodrigo Soares LEITE, Jandecy Cabral; LEITE, Jandecy CabralThe wind energy matrix has been gradually increasing in recent years and its importance for the renewable energy industry is increasingly linked to environmental benefits. This study applies the NARX model to forecast wind speed in the short term and, consequently, wind energy generation. The research used data from the SONDA project (System of National Organization of Environmental Data), organized by INPE, specifically from the Brasília anemometric station, covering the period from February 2005 to March 2019. The results indicate that the NARX model achieved better performance for short-term forecasts of 10 minutes up to 10 steps ahead, providing greater reliability in wind energy delivery for the energy sector.Item Development of Intelligent Devices for Communication and Data Pre-Processing in the Process of Electronic Meter Testing(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) RAMOS JUNIOR, Juarez da Silva LEITE, Jandecy Cabral GOMES, Marivan Silva PAULA, Railma Lima de CARVALHO, Michael da Silva SILVA, Ítalo Rodrigo Soares SIQUEIRA JUNIOR, Paulo Oliveira PARENTE, Ricardo Silva MIRANDA, Luís Gabryel dos Santos; LEITE, Jandecy CabralEfficient communication between electronic devices in production lines is essential for process maturity in Industry 4.0. This study discusses the development of an intelligent Lean Manufacturing system for integrating communication and supporting decision-making in electronic meter testing. The proposed system includes embedded firmware for data collection and transmission, demand forecasting algorithms, and a dashboard with production indicators. The results indicate that implementing this technology reduces costs and improves final product quality.Item Application of the Nonlinear Autoregressive Model with Exogenous Inputs for River Level Forecast in the Amazon(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) LOPES, Gisele de Freitas NASCIMENTO, Manoel Henrique Reis LIMA, Alexandra Amaro de MORAES, Nadime Mustafa PINTO JÚNIOR, José Roberto Lira ALENCAR, Ana Priscila Barbosa de ALENCAR, David Barbosa de; LEITE, Jandecy CabralThis work is justified by three key aspects: the use of Artificial Intelligence, the problem of floods in the Amazon, and the application of technology in decision-making. Environmental impacts caused by economic and social factors, such as floods and river ebbs, affect public health and agricultural production, as well as increase erosion in risk areas. Thus, the use of AI to predict river levels can minimize these impacts by enabling effective preventive actions. The study applies the Nonlinear Autoregressive Model with Exogenous Inputs (NARX) to forecast river levels in the Amazon, using easily accessible variables implemented in MATLAB. The results indicate that the NARX model can accurately predict river level fluctuations, contributing to environmental impact mitigation strategies.Item Modelo de avaliação de saneamento básico usando Sistema de Inferência Fuzzy(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) NASCIMENTO, Aline Santos do; ALENCAR, David Barbosa de; NASCIMENTO, Manoel Henrique Reis; SANTOS, Lucélia Cunha da Rocha; MORAES, Nadime Mustafa; LEITE, Jandecy CabralThis study developed a fuzzy inference model to assess the quality of basic sanitation services. The model analyzed indicators such as water supply, sewage, and waste management, using fuzzy methodology to address uncertainties and generate more accurate diagnoses. The results demonstrated that fuzzy logic is effective in identifying critical points in sanitation, offering solutions to reduce costs and increase efficiency. The model is adaptable to different regional contexts, facilitating its application in other locations and supporting the fulfillment of SDG 6.Item Kanban Eletrônico com Inteligência Artificial: Desenvolvimento e Implementação de uma Solução para Transformação Digital e Otimização Produtiva em uma Indústria de Fitas do PIM(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) ARAÚJO, Lívia Fernanda Lobão de; GUIMARÃES, Gil Eduardo; MARINELLI FILHO, Nelson; SCHMIDT, Fabricio Carlos; CORREA, Geraldo Nunes; GUIMARÃES, Gil EduardoThis study details the development and application of an electronic Kanban system integrated with Artificial Intelligence (AI) in a company from the Industrial Pole of Manaus (PIM). The research follows the principles of Industry 4.0, focusing on optimizing production sequencing, improving operational efficiency, and reducing costs. The methodology combined exploratory and applied approaches, employing qualitative and quantitative methods to map bottlenecks and create customized technological solutions. The results demonstrate significant advances, including a 67% reduction in order registration time, a 22% increase in overall equipment efficiency (OEE), and an 18% reduction in non-conformities identified in the final inspection. The study highlights the transformative impact of digitization and automation on modernizing PIM companies and presents a practical and replicable model to address similar challenges in the Brazilian industrial context.Item Application of Automation and Computer Vision in Reducing Failures in the Production Process of Safety Belts(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOUZA, Kerlisson Silva de; SANTOS, Eliton Smith dos; ALENCAR, David Barbosa de; NASCIMENTO, Manoel Henrique Reis; SANTOS, Alyson de Jesus dos; LEITE, Jandecy CabralProduct quality is a key factor for companies to stand out in a highly competitive market. In the Manaus Industrial Hub (PIM), defect detection in safety belts is a crucial stage in the production process. This study proposes the automation of this process using computer vision and artificial intelligence (AI). The developed system captures images of the parts and applies Deep Learning techniques to identify defects. The results demonstrated 100% accuracy in defect detection, indicating that the proposed solution is effective in improving reliability and reducing waste in the manufacturing of safety belts.