Dissertação PPG.EGPSA
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/3
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22 resultados
Resultados da Pesquisa
Item Avaliação de imunidade eletromagnética radiada do protocolo IOT LORA(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) RIBEIRO, Jhonatan; SANTOS, Eliton Smith dosIndustrial 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.Item Técnicas da indústria 4.0 aplicadas na melhoria do processo de corte dos terminais das baterias de íon-lítio(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) MACIEL, Lincoln Fábio Luiz; NASCIMENTO, Manoel Henrique ReisThe transition to Industry 4.0 has brought significant advances in production processes, where the application of Artificial Intelligence (AI) technologies has transformed entire sectors, including the manufacturing of lithium-ion batteries. This work addresses the development of an intelligent system focused on improving the cutting of battery terminals, using AI algorithms to optimize precision and reduce defect rates. The central problem is the imprecision in cutting the terminals of lithium-ion batteries, leading to a high rate of defective or unusable products. This issue not only increases production costs due to material waste but also negatively impacts the overall efficiency of the manufacturing process. To achieve this goal, a methodology was used for the application of machine learning and real-time data analysis, which enabled automatic adjustments in the cutting process, promoting a flexible and adaptable manufacturing environment. The results obtained from the application of the system indicate a significant reduction in the rate of defective batteries, as well as an increase in the quality and uniformity of the produced terminals. This efficiency gain demonstrates the potential of AI to meet the requirements of quality and precision while simultaneously reducing production costs. The integration of AI technologies in the process of cutting lithium-ion battery terminals not only promotes improvements in quality and sustainability but can also generate a significant competitive advantage for the renewable energy and storage sector in flexible manufacturing processes.Item Sistema integrado para detecção de falhas do processo de montagem de placas utilizando ferramenta de Business Intelligence para maturidade da Indústria 4.0.(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) PAIXÃO, Elisete da Silva; CAMPOS, Paola SoutoThe plate assembly process in a company has a target of producing 2000 plates per day; however, 10% of this production presents failures, compromising the management-defined goals. This study aims to implement an intelligent system for identifying and correcting failures in the board assembly process, based on Business Intelligence, aiming at the evolution of Industry 4.0 practices. The methodology included process mapping, analysis of requirements for implementing the integrated system, and evaluation of the system's effectiveness. The integrated system enabled communication between the various systems in the production process, providing more accurate information to improve usability performance and ensure data security. The implementation of this system represented a significant advancement for the evolution of Industry 4.0 practices, providing improvements in quality, efficiency, and cost reduction.Item Desenvolvimento de um dispositivo inteligente para monitoramento de subestações elétricas integrado à plataforma SGE: uma aplicação da Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SANTOS JUNIOR, Hélio Andrade dos; ALENCAR, David Barbosa deThe dissertation focuses on the development of a portable device for monitoring electrical substations, integrated with the SGE platform, utilizing Industry 4.0 technologies like the Internet of Things (IoT) and real-time data analysis. The primary objective was to create a solution to identify and mitigate energy losses, optimize consumption, and reduce operational costs. To achieve this, the research employed an inference model based on fuzzy logic and developed a real-time process control (RTPC) framework. The device and its monitoring software were validated through field tests in real substations, showing positive results in reducing losses and improving energy efficiency. This work contributes to the modernization of electrical substation management, aligning with sustainability trends and technological advancements in the energy sector.Item Utilização de Inteligência Artificial e IoT para Inspeção e Detecção de Defeitos em Placas de Circuito Impresso(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SOARES, Karen Kettelen Souza; GUIMARÃES, Gil EduardoThe increasing complexity in the manufacturing of printed circuit boards (PCBs) has posed significant challenges for industries, particularly in the inspection and detection of defects. This study proposes the use of Artificial Intelligence (AI) and the Internet of Things (IoT) as tools to optimize fault detection in PCBs, focusing on reducing human errors and improving operational efficiency. The research is based on the implementation of an automated inspection system, utilizing neural networks for defect classification and IoT sensors for real-time monitoring of manufacturing conditions. The main goal of the dissertation is to develop an efficient analysis model to identify early faults, enhance product quality, and reduce operational costs. The study involved building a functional prototype that integrates AI and IoT, validated in a real production environment. The results showed that the proposed approach could detect defects with higher accuracy, leading to significant improvements in the efficiency of the PCB production line.Item Aplicação de Tecnologia da Indústria 4.0 na Melhoria dos Processos de Injeção Plástica em uma Empresa no Polo Industrial de Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) FERREIRA, Marcos Sidney Castro; CAMPOS, Paola Souto; LEITE, Jandecy CabralOptimizing lead times ensures that customer deadlines are met, thereby promoting customer satisfaction and loyalty. Additionally, reducing lead time leads to more efficient resource management, directly impacting the reduction of operating costs. This study aimed to implement Industry 4.0 technologies to improve plastic injection processes at a company in the Manaus Industrial Estate (PIM), reducing manufacturing costs, accelerating the lead time for device construction, and optimizing the time for developing new products. The methodology involved data collection on new production processes and the evaluation of the technology's impact on reducing manufacturing costs and improving lead time. This was done by mapping the current process flow, analyzing production loss reports, and examining production performance data. The results show a significant reduction in lead time, from 35 to 9 days. 3D printing also enabled the creation of more complex and precise devices, with less material waste, directly contributing to cost reduction and improved product quality.Item Smart energy: aplicação do sistema fotovoltaico utilizando algoritmos genéticos para tomada de decisão na Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) WASCHINGTON, Adriana Carneiro; SILVA, Simone daThe global energy transition and the need for energy efficiency in industrial environments are driven by the search for sustainability and the reduction of environmental impacts. This work addresses the application of genetic algorithms in the management of photovoltaic systems within the context of Industry 4.0, highlighting the concept of Smart Energy. The main objective is to investigate the benefits and impacts of this approach on energy efficiency, environmental sustainability, and the reduction of operating costs at the Manaus Industrial Estate (PIM). To achieve the objectives, methods based on computer simulation and analysis of real cases were used. The research included the modeling and development of genetic algorithms capable of optimizing variables such as energy generation, storage, and consumption in photovoltaic systems. Data was collected based on local climatic conditions, energy demand profiles, and industrial operating parameters. The results indicated that the genetic algorithms enabled significant gains in energy efficiency, with an average reduction of 20% in energy waste and 15% in operating costs. In addition, the model developed proved to be effective in adapting to climate variations and industrial demands, reducing dependence on non-renewable sources and greenhouse gas emissions. The conclusion is that integrating photovoltaic systems with genetic algorithms is a promising solution for energy management in Industry 4.0, promoting sustainability and industrial competitiveness, especially in regions with high solar incidence like the Amazon. The research highlights the relevance of technological innovation in the transition to a low-carbon economy.Item Indústria 4.0: uma proposta para implementação em uma empresa metal mecânica no Polo Industrial de Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) PEREIRA, Danilo Serrão; GUIMARÃES, Gil EduardoThis work presents a proposal for the implementation of Industry 4.0 technologies in a mechanical metal company located in the Industrial Pole of Manaus (PIM). The research addresses the company's maturity level regarding Industry 4.0 using the Impuls questionnaire as the main tool. Through detailed analysis, the key gaps and opportunities for the company's digital transformation are identified, focusing on areas such as automation, data analytics, and system integration. The study proposes a roadmap for the implementation of Industry 4.0 technologies, with specific steps for automating production processes and workforce training. Additionally, challenges such as resistance to change and the shortage of qualified labor are discussed, along with strategies to overcome them. The research concludes that transitioning to Industry 4.0 is essential to enhance the company's competitiveness, promote sustainability, and improve operational efficiency.Item Criação de aplicações para sistema de teste funcional com rastreabilidade para carregadores de celular(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) MAUÉS, Elvis Jardim; MARINELLI FILHO, NelsonThis project addresses the growing need to guarantee the traceability and quality of cell phone chargers in a globalized market, where transparency in processes is fundamental. The main objective is to carry out electrical tests with traceability using QR code reading and programming using National Instruments' LabWindows/CVI software. The study begins with the contextualization of the problem, highlighting the importance of traceability to guarantee the quality and reliability of electronic products, such as cell phone chargers. The need to implement methods that speed up the testing process was observed, aiming to maximize production efficiency. The materials and methods used involve the use of LabWindows/CVI software, an ANSI C development environment specific to test and measurement applications. In addition, observations were carried out in the charger testing sector to identify gaps in the process and determine the best strategies to implement traceability. In the proposed process, the first step consists of reading the QR code on the ruler and chargers, organizing them in numerical sequence for recording in a .txt file. Next, the second step performs data collection, performs the necessary electrical tests, and generates a .csv file for local storage, along with an .xml file for recording in the company database. After the first test is completed, each charger undergoes an evaluation to verify if it has passed. If the charger passes the previous test, it is allowed to proceed to the next one. Otherwise, the system blocks further tests until the identified issue is resolved. Results include significant improvement in efficiency of the testing process, ensuring complete traceability of chargers and optimizing equipment production time.Item Arquitetura computacional para controle de monitoramento de qualidade de energia utilizando ferramentas de inteligência artificial focado na Indústria 4.0 com integração de energias renováveis(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) KAMIO, Edson; GUIMARÃES, Gil EduardoElectric power quality (QEE) is crucial for the efficiency of industrial systems in Industry 4.0. This dissertation proposes an innovative computational architecture to control and monitor QEE, integrating renewable energy sources and artificial intelligence (AI). Key metrics like power factor (PF) and total harmonic distortion (THD) directly influence operational costs and equipment lifespan. AI-based predictive strategies dynamically manage capacitor banks and harmonic filters, mitigating issues such as inadequate PF and high THD. Reactive power compensation (CPR) is central to improving PF, reducing system losses, and enhancing energy efficiency. The system also integrates solar energy management, optimizing energy savings and sustainability. Data is collected using IMS Smart Cap 485 devices via the Modbus-RTU protocol, measuring voltage, current, active/reactive power, and THD. Stored in a MySQL database, the data is analyzed in real time with deep learning (LSTM) and optimization (AG) algorithms. Python-based dashboards visualize data, predict network issues, and support strategic actions, such as activating capacitors and THD filters. This approach highlights AI's role in energy efficiency and reducing reliance on conventional sources. Gaps in literature include the lack of interoperability standards, the need for explainable AI algorithms, and limited longitudinal studies on real-world applications. This research addresses these challenges, contributing to Industry 4.0's sustainability and efficiency goals.
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