https://repositorio.ufu.br/handle/123456789/5466 Sat, 22 Aug 2026 15:39:16 GMT 2026-08-22T15:39:16Z https://repositorio.ufu.br/handle/123456789/49588 Title: Desenvolvimento de módulos robóticos para instalação de amortecedores de vibração em cabos de alta tensão Abstract: The application of robots in high-voltage power line maintenance operations has gradually advanced over the past decades, with notable emphasis on mobile robot systems. However, despite the progress achieved, many challenges related to the development of such robots persist. Among the desirable features for these robots are: reliability, autonomous operation, low weight, high obstacle-transposing capability, and shielding against electromagnetic interference, many of which remain insufficiently addressed in existing robotic systems. Considering this, the present dissertation proposes the development of robotic modules for the installation of vibration dampers on high-voltage power lines. These robotic modules were designed to perform specific maintenance tasks in energized and hard-to-reach environments. This work describes the design, development, construction, and experimental testing of two robotic modules: one for the installation of Stockbridge-type dampers, and another for the installation of preformed-type vibration dampers. The dissertation also presents a shielding procedure for the robotic modules against electromagnetic interference and electrostatic discharges originating from transmission lines of up to 138 kV. Field tests carried out at the facilities of a power utility company demonstrated the modules’ effectiveness in overcoming a significant portion of existing limitations, positioning the developed solutions as promising alternatives in the field of high-voltage line maintenance robotics. Mon, 23 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49588 2026-02-23T00:00:00Z https://repositorio.ufu.br/handle/123456789/49301 Title: Implementação de modelo de folga de backlash dinâmico em engrenagens para análise de dinâmica de rotação Abstract: Gearboxes are essential components in industrial rotating machinery, but nonlinear phenomena such as dynamic backlash and time-varying mesh stiffness (TVMS) introduce high complexity to their overall structural response. Thus, it becomes necessary to develop tools capable of adequately representing system behavior and aiding in the understanding of the involved phenomena. In this context, the objective of this study was to develop a mathematical model capable of interpreting such effects and relating them to rotational dynamics. To this end, a nonlinear meshing model was coded in Python and integrated into the Rotordynamic Open-Source Software (ROSS) library, based on the Finite Element Method (FEM). The original formulation of 3 degrees of freedom (DOF) per gear was expanded to 6 DOF per node, enabling the inclusion of geometric parameters and helical gear forces. Additionally, a hyperbolic tangent smoothing technique was incorporated to handle numerical discontinuities and optimize the convergence of time integrators. To solve the differential equations of motion, the Newmark method combined with the Newton-Raphson algorithm and adaptive time stepping was used. A simplified analytical model was also developed to determine the natural frequencies of the gear pair. The results were validated against literature, showing natural frequencies perpendicular and parallel to the Line of Action (LOA). Bifurcation diagrams confirmed clear transitions between periodic and chaotic motion regimes at high speeds. Finally, the integrated methodology was applied to a real practical case of a large-scale industrial gearbox operated by Petrobras (input rotation of 1953 rpm and output of 12225 rpm) employing the full mesh stiffness profile calculated by ROSS. For this case, the smoothing tool indicated a reduction of approximately 7.3% in total computational processing time without loss of physical representativeness of the dynamic system. It is concluded that the developed approach represents a solution for mechanical design and fault diagnosis in complex industrial transmissions Mon, 27 Jul 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49301 2026-07-27T00:00:00Z https://repositorio.ufu.br/handle/123456789/49169 Title: Comportamento de revestimentos de ferramentas de corte no fresamento do Inconel 718 Abstract: This study aimed to investigate the influence of different coatings applied to solid carbide end mills in the end milling of Inconel 718, a nickel-based superalloy characterized by high mechanical strength and low machinability. Three commercial coatings (ALCRONA EVO® (AlCrN), TISAFLEX® (AlTiN/TiSiXN), and LATUMA® (AlTiN)) were evaluated, in addition to an uncoated condition, under different combinations of cutting speed (50 and 80 m/min) and feed per tooth (0.035 and 0.070 mm/tooth). The experimental tests included the analysis of tool life, surface roughness, cutting temperature, cutting forces, and chip morphology. Tool life was determined based on a maximum flank wear criterion of 0.3 mm and expressed in terms of volume of material removed. Surface roughness was evaluated using the Ra parameter, temperature was measured by infrared thermography under dry conditions, and cutting forces were monitored using a piezoelectric rotating dynamometer. Chip morphology was qualitatively analyzed using optical microscopy. The results showed that tool performance was significantly influenced by the coating, with TISAFLEX® exhibiting the best performance and longest tool life, while the uncoated tool showed the poorest results. Feed per tooth was identified as the most influential parameter, leading to reduced tool life and increased surface roughness (Ra) and cutting forces. Temperature measurements presented relatively low values, attributed to the short cutting length, although an increasing trend was observed along the cutting pass. The chips exhibited similar geometry across the evaluated conditions, being more influenced by the cutting parameters than by the coating. Overall, the results demonstrate that the proper combination of coating and cutting parameters is essential to improve the milling of Inconel 718, contributing to enhance the tool life and surface quality. Tue, 21 Jul 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49169 2026-07-21T00:00:00Z https://repositorio.ufu.br/handle/123456789/49158 Title: Aplicação de redes neurais convolucionais a imagens de modelos térmicos para a estimativa de tamanho e posição de inclusões que simulam tumores mamários Abstract: Breast cancer is the most prevalent and deadly neoplasm among women worldwide, highlighting the urgent need for early detection strategies that are accurate, safe, and accessible. Mammography, while the gold standard, has limitations, including repeated radiation exposure, reduced sensitivity in dense breasts, and reliance on expert interpretation, while ultrasound and magnetic resonance imaging increase cost and complexity. Infrared thermography emerges as a non-invasive, low-cost, and radiation-free alternative, but visual inspection alone lacks the sensitivity to detect deep or small tumors. In this context, machine learning methods have shown promise in extracting subtle thermal patterns beyond human perception. This study implements and trains convolutional neural networks in Python using the Tensorflow library to learn the position and diameter of a spherical inclusion in thermal models, using steady-state temperature maps generated through finite element simulations in Ansys Mechanical. The methodology is applied to two thermal models: i) parallelepiped: simulating an experiment performed on a silicon parallelepiped with inclusions of spheres heated by thermal resistors, the neural network trained with 270 matrices (27x39) achieved an accuracy of 99.10% and a sensitivity of 98.10%, and was able to correctly predict the position and diameter of the inclusion when applied to an experimental thermogram. ii) anatomical model: simulating metabolically active tumor tissue immersed in anatomical breast geometry representing healthy breast tissue, the neural network trained with 965 matrices (50x50) achieved an accuracy of 97.63% and a sensitivity of 95.62%. These results demonstrate that a neural network can reliably learn geometric and physical characteristics related to inclusions (which simulate the presence of tumors) from low-resolution thermal data, indicating the feasibility of using artificial intelligence applied to thermal images for tumor detection. Thu, 26 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/49158 2026-02-26T00:00:00Z