Artigos
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/5
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Item Optimization of the Manual Insertion Process of Electric Motor Winders Using Lean Manufacturing Techniques(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) SOUZA, Renê Brito de LEITE, Jandecy Cabral; LEITE, Jandecy CabralDuring the manual insertion process analysis in electric motor production, process losses were identified. Using Lean Manufacturing tools and data collection, gaps in line balancing were mapped, resulting in low efficiency and desaturation in the insertion process. This study proposes the application of computer simulation and industrial layout restructuring based on NR-12 safety standards, in addition to implementing improvements such as the use of the spaghetti diagram and Kaizen techniques. The results indicate an average reduction of 30% in handling time, ergonomic improvements, better organization of the production space, and a reduction in intermediate inventories, consolidating an efficient and replicable model in other industrial units.Item Fuzzy System for Fault Detection in Electric Motors for Aluminum Casting(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) SILVA, Lenildo Marcos da Mota; BRITO JUNIOR, Jorge de Almeida; LEITE, Jandecy Cabral; ALENCAR, David Barbosa de; NASCIMENTO, Manoel Henrique Reis; QUEIROZ JÚNIOR, Fernando Cardoso de; LEITE, Jandecy CabralThis study presents the development of a fuzzy logic-based system for predictive fault detection in electric motors used in aluminum casting processes. The addressed problem concerns the need to optimize predictive maintenance in a competitive industrial environment, minimizing unexpected downtimes and costs associated with corrective maintenance. The main objective was to create a fuzzy algorithm for real-time monitoring of critical variables such as temperature, pressure, and electric current. The methodology involved simulations of operational scenarios validated through experimental tests in a controlled environment. Results indicate that the proposed fuzzy system accurately identifies anomalies and issues preventive alerts, contributing to extending motor lifespan and improving operational efficiency. It is concluded that the developed solution can be integrated into industrial supervisory systems, enhancing reliability and productivity.