Dissertação PPG.EGPSA

URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/3

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2023 - 20252

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Data inicial: 2023Assunto: search.filters.subject.Aprendizado de Máquina

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Sistema Inteligente de Verificação de Pedidos com Visão Computacional e Aprendizado de Máquina para Expedição Industrial 4.0
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) THEOCHAROPOULOS, Scarlette Silva; CAMPOS, Paola Souto
    This dissertation presents the development and evaluation of the PRS (Poligonal Reconnaissance System), designed for automated order verification in industrial environments within the context of Industry 4.0. The main objective was to develop an intelligent order verification system based on computer vision and machine learning, integrating image analysis, depth simulation, and hybrid validation (fixed rules and AI), with a focus on applications in automated shipment processes. The adopted methodology included the use of computer vision techniques to identify and calculate packaging areas in simulated images, depth simulation to correct perspective distortions, and a supervised Random Forest model trained with historical weight and area data. The resulting hybrid system demonstrated accuracy above 94% in detecting volume and weight inconsistencies, significantly reducing verification time and operational rework .
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    Desenvolvimento de sistema inteligente de manufatura enxuta para gerenciamento e controle da produção do medidor eletrônico.
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2023-10-20) RAMOS JÚNIOR, Juarez da Silva; LEITE, Jandecy Cabral; http://lattes.cnpq.br/7279183940171317
    This project presents a viable solution to the problem of communication, integration and control of information in the manufacturing process of the electronic meter, a product that includes an Intelligent System. The aim was to develop an Intelligent System for Lean Manufacturing to support decision-making when using Production Control processes to guarantee product quality and reduce production costs. The advancement of technologies in manufacturing processes aligned with methodologies that enable production efficiency, this project demonstrates a focus on the modernization of factories by using the pillars of industry 4.0 to enable new investment perspectives and fit the company into the process maturity requirements. The software was developed for a company in the Manaus Industrial Estate (PIM). It used Machine Learning, Deep Learning and various methods such as: optimization, bio-inspired, classification and pattern recognition, heuristic, iterative and Newton methods. The results showed that the main development phases were validated, obtaining a computational model applied to production demand forecasting with knowledge generation made available on production and quality dashboards and the Intelligent System with integration via API and development stacks with modern resources (web frameworks), developing with quality metrics: reliability, security, availability and robustness.