Navegando por Autor "SANTOS, Eliton Smith dos"

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Algoritmo para otimizar o processo de inspeção de qualidade
(Instituto Nacional da Propriedade Industrial (INPI), 2024-07-22) SOUSA, Marcio Rizonildo Aquino de; SANTOS, Eliton Smith dos; CAMPOS, Paola Souto; NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa de
Document for the registration of computer software by the National Institute of Industrial Property (INPI), validating the software "Algorithm to optimize the quality inspection process", developed in Python, valid for 50 years.
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 Leite
This 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.
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
This study investigates the technologies, methods and challenges involved in drying and curing paints and varnishes applied to reflective strips, with emphasis on Industry 4.0-based solutions. It proposes an integrated hardware–software model for automatic detection of curing level through light radiation. A controlled-environment prototype and real-time control system aim to optimize the process, accelerate UV photopolymerization and improve product quality.
Avaliação de imunidade eletromagnética radiada do protocolo IOT LORA
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) RIBEIRO, Jhonatan; SANTOS, Eliton Smith dos
Industrial automation, historically based on wired technologies, has evolved toward wireless solutions, driven by the increasing interconnection of devices and the need to overcome logistical and range limitations. In Industry 4.0, wireless communication plays a central role by integrating sensors, actuators, and control systems into an intelligent production environment. This integration enables real-time data collection and analysis, facilitating automation, optimization, and process flexibility. Despite the benefits, adopting wireless technologies faces significant challenges, particularly electromagnetic interference (EMI), which can compromise the operation of devices and systems. This study presented an analysis of the electromagnetic immunity of the LoRa protocol, using the Heltec V2 board, which incorporates the SX1278 chip, to evaluate its robustness in environments subject to electromagnetic interference. The tests, conducted according to the IEC 61000-4-3 standard, identified critical failure frequencies such as 232.4 MHz, 412 MHz, and 615.2 MHz under different parameter configurations. In these conditions, partial or total communication loss was observed, depending on the intensity of the irradiated field, demonstrating that different configurations can significantly impact the system's immunity. Solutions such as using robust protocols, including LoRaWAN and NB-IoT, spectral spreading techniques, electromagnetic shielding, and filters can mitigate the effects of EMI. Furthermore, future strategies include developing adaptive algorithms to dynamically adjust communication parameters and signal failures, enhancing the resilience and reliability of the LoRa protocol in complex industrial environments. The results of this research contribute to improving wireless communications in Industry 4.0, highlighting LoRa as an essential technology for robust and efficient connectivity.
Desenvolvimento de um sistema inteligente para secagem e cura autônoma na fabricação de produtos autoadesivos
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SOUZA, Raimundo Alberto Farias de; SANTOS, Eliton Smith dos
The production of reflective strips requires high precision and efficiency, especially for traffic signage and automotive applications. However, traditional drying and curing methods for chemical layers, such as paints and varnishes, rely on long outdoor periods, compromising product quality and reducing productivity. This study proposes the development of an integrated hardware and software model that allows automatic detection of the curing level, using controlled light radiation sources to accelerate the drying process and ensure greater uniformity. The research includes the creation of a controlled environment prototype and a Human-Machine Interface (HMI) monitoring system to optimize industrial production. The theoretical foundation is based on the application of ultraviolet (UV) photopolymerization technology, aiming to improve product quality, production efficiency, and process sustainability.
Eficiência Produtiva em Linhas SMT: Aplicação da Métrica OEE e Estratégias de Melhoria na Indústria Eletroeletrônica da Zona Franca de Manaus
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) MEDEIROS, Gelson Ricardo Martins; ALMEIDA, Luiz Fernando Correia de; SANTOS, Eliton Smith dos
This article presents the results of an applied study on production efficiency in a Surface Mount Technology (SMT) assembly line within the electronics manufacturing sector of the Manaus Free Trade Zone. Through the application of the Overall Equipment Effectiveness (OEE) metric, the study aimed to diagnose operational bottlenecks and propose targeted interventions using tools such as the Ishikawa Diagram and SMED method. Data collected between January and June 2023 revealed significant losses in availability and performance, leading to an OEE of only 22.4%—far below the world-class benchmark of 85%. Corrective actions including predictive maintenance, shift reorganization, and digital control via MES contributed to reducing downtime and improving operational governance. The study concludes that OEE is not merely a technical metric but a strategic tool for organizational transformation and Industry 4.0 integration.
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 dos
Product 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.
Simulação Computacional no Auxílio à Tomada de Decisão nos Processos Industriais: Utilizando a Ferramenta Tecnomatix Plant Simulation 9.0
(Universidade Federal do Pará, 2011) SANTOS, Eliton Smith dos; QUARESMA, João Nazareno Nonato
The research presents the use of computer simulation as a tool to aid decision-making in industrial processes, using the Tecnomatix Plant Simulation 9.0 software. The study was applied in a production line of 21" TV sets at the Manaus Industrial Hub, aiming to evaluate the line's performance and propose improvements. The simulation allowed the identification of bottlenecks, the balancing of the production line, and the optimization of resource usage, resulting in a 6.6% increase in daily production. The tool proved effective for preventive and predictive analysis, enabling virtual experiments without interfering with real production.
SIMULAÇÃO COMPUTACIONAL NO AUXÍLIO À TOMADA DE DECISÃO NOS PROCESSOS INDUSTRIAIS: UTILIZANDO A FERRAMENTA TECNOMATIX PLANT SIMULATION 9.0
(Universidade Federal do Pará, 2011) SANTOS, Eliton Smith dos; QUARESMA, João Nazareno Nonato
Currently, there are numerous automated production processes that are becoming increasingly complex due to the demands of the modern world. These processes require increasingly powerful engineering tools for efficient modeling and analysis. In this context, the computational tool Tecnomatix Plant Simulation 9.0 emerges as a means of optimizing industrial production. This study presents the application of this tool in modeling and simulating a production line, highlighting its relevance for production management by reducing costs and maximizing profits. The approach is demonstrated through a case study in the electronics industry, analyzing process efficiency and proposing improvements for time and resource optimization.