Navegando por Autor "GUIMARÃES, Gil Eduardo"

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A transformação digital e os avanços tecnológicos na Zona Franca de Manaus: impactos e desafios da implementação da Indústria 4.0 em direção à Sociedade 5.0
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) VALE, Rosangela Victor do; GUIMARÃES, Gil Eduardo
The implementation of Industry 4.0 principles in the Manaus Free Trade Zone represents a significant opportunity to boost industrial competitiveness and sustainable development in the region. By integrating technologies such as the Internet of Things (IoT), big data, artificial intelligence, and automation, local companies can improve their production processes, increase operational efficiency, and reduce costs. However, for this transformation to be effective, continuous investment in workforce training, technological innovation, and digital infrastructure is essential, preparing the region for the challenges of the global economy. The transition to Society 5.0, focusing on creating an economy centered on human needs, offers a promising outlook for the Manaus Free Trade Zone. The combination of advanced technologies with a humanized approach allows the region to not only increase its industrial competitiveness but also contribute to the creation of a more balanced socioeconomic model. This dissertation investigates how the implementation of Industry 4.0 technologies in the Manaus Industrial Hub can facilitate the transition to Society 5.0, promoting industrial efficiency, technological innovation, and improvements in the quality of life of the local population.
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 Eduardo
Electric 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.
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 Eduardo
This 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.
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 Eduardo
This 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.
Metodologia para Avaliar Nível de Maturidade da Indústria 4.0 e sua Correlação com Atributos do Metaverso Industrial
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) SOUZA, Patricia Nunes de GUIMARÃES, Gil Eduardo VIEIRA JUNIOR, Milton BERSSANETTI, Fernando Tobal MARINELLI FILHO, Nelson; GUIMARÃES, Gil Eduardo
This article proposes a methodology to assess the maturity level of Industry 4.0 tools and their correlation with the attributes of the industrial metaverse. The study aims to help companies of different sizes identify the current stage of implementation of Industry 4.0 enabling technologies, with the goal of preparing the groundwork for the development of metaverse applications. The methodology was applied to a company in the Manaus Industrial Hub, allowing for the assessment of the maturity level of the tools and the identification of critical points for future investments. The results showed that the company has intermediate maturity in tools such as IoT and big data, but has gaps in areas such as virtual reality and digital twins. The study concludes that the integration of the metaverse into Industry 4.0 can bring significant benefits, such as increased productivity and cost reduction, but faces challenges such as the need for robust technological infrastructure and employee training.
Transformação digital no Polo Industrial de Manaus (PIM): desenvolvimento e implementação de um sistema de Kanban eletrônico utilizando inteligência artificial para otimização e sequenciamento de produção em uma empresa de fitas do PIM
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) ARAÚJO, Lívia Fernanda Lobão de; GUIMARÃES, Gil Eduardo
This thesis discusses the development and implementation of an electronic Kanban system integrated with artificial intelligence (AI) to optimize the production sequencing process in a company located in the Industrial Pole of Manaus (PIM). The main objective of this work is to enhance operational efficiency, integrate production processes, and reduce costs through the adoption of Industry 4.0 technologies. The study explores the application of electronic Kanban as an automation tool, integrated with AI, to manage production more efficiently and transparently. The developed system aims to optimize resource allocation and supply chain management, also offering improvements in quality control and process traceability. The results demonstrate the system’s effectiveness in increasing productivity, reducing errors and rework, and enabling real-time adjustments in the production process.
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 Eduardo
The 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.