EST - Trabalho de Conclusão de Curso Graduação

URI permanente para esta coleçãohttps://ri.uea.edu.br/handle/riuea/4795

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Aplicação de redes neurais artificiais na predição de perfurações durante a soldagem de juntas tubulares em um Chassi Baja
(Universidade do Estado do Amazonas, 2022-05-30) Guimarães, Pedro Henrique Ferreira; Cruz Neto, Rubelmar Maia de Azevedo; Soeiro Junior, Jaime Casanova; Alves, Antonio do Nascimento Silva
The quality of welded joints in an automobile chassis is essential to ensure safety and performance. In order to reduce cost, meet deadlines and ensure maximum quality, knowledge about adjustment settings and their adjustment is fundamental for operations. The objective of this study is to train an artificial neural network for prediction of burn-through in welded joints using the GMAW process with ER70S-6 electrode in SAE 1020 steel tubes of a Baja SAE chassis. Electrode wire diameter, type of shielding gas, source voltage and feed speed were used as predictor variables. Two weld beads are welded to each specimen, following the configurations determined by a DOE (Design of Experiments). Following the weldings, it was observed which bodies showed burn-through in the base metal. After the observations, a PMC network was trained in Python and the model's responses were compared to the tests. The model presented an accuracy of 78% and a sensitivity of 76% for data, which was not trained, revealing the potential of using artificial neural networks to predict burn-through in welded joints.
Influência dos parâmetros de processo na fabricação de sessões de tubo metálicas via manufatura aditiva por deposição a arco.
(Universidade do Estado do Amazonas, 2022-05-30) Andrade, Gilda Gabrielly Souza de; Cruz Neto, Rubelmar Maia de Azevedo; Soeiro Junior, Jaime Casanova; Soeiro Junior, Jaime Casanova; Alves, Antonio do Nascimento Silva
Additive Manufacturing is increasingly taking space in the world industry and establishing itself as a good alternative to subtractive technologies. One of the areas of additive manufacturing that has shown to be very promising is Wire + Arc Additive Manufacturing (WAAM), which stands out due to its high deposition rate and the possibility of creating complex geometries with a great Buy-To-fly ratio, when compared, for example, to the machining process. In MADA, the deposition process by GMAW (Gas Metal Arc Welding) is highlighted, where the electric arc is used as a heat source. This work sought to analyze the geometric behavior of cylindrical parts manufactured by MADA, through experiments varying three main characteristics: Interpass temperature range, number of layers and diameter of the tube section. After manufacturing the experiments, a 3D scanner was used to generate a CAD model of the welded parts (in order to obtain the real dimensions of them) and an inspection of the heights was carried out through a comparison surface between the generated mesh. by the scanner and a “standard” 3D model. After the statistical analyses, only a trivial model was found, which, for the design used in this work, shows that the Number of Layers is the parameter with the greatest influence on the height of the experiments.