Publicación: Estudo da influência das geometrias da ferramenta nas tensões residuais geradas na usinagem por torneamento do aço SAE 1050
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2024-10-22
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info:eu-repo/semantics/openAccess
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http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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Universidad Nacional de Educación a Distancia (España), Universidad de Concepción - Chile. Departamento de Ingeniería Mecánica
Resumen
Nos processos de fabricação por usinagem, as tensões residuais advêm da combinação dos esforços mecânicos do contato ferramenta/peça e do calor gerado no processo. Os parâmetros de corte, bem como a geometria das ferramentas de corte empregadas resultam em diferentes campos de tensões residuais. Assim sendo, o processo de torneamento introduz tensões residuais nos componentes fabricados, que dependendo da natureza, distribuição e magnitude, podem ser benéficas ou deletérias influenciando a vida em serviço e a resistência à fadiga da peça. O presente trabalho tem como objetivo avaliar a influência de diferentes geometrias de ferramenta de corte nas tensões residuais, nas forças de corte e na integridade superficial geradas no torneamento do aço SAE 1050 com insertos de metal duro (cemented carbide tool). A análise das tensões residuais foi realizada por difração de raiosX (XRD), pelo método do sen²ψ. Por fim, foi desenvolvida uma Rede Neural Artificial (RNA), que demonstrou eficiência na predição das tensões residuais e da rugosidade para o material estudado. Os resultados indicaram que o torneamento gerou tensões residuais de maiores magnitudes na direção de corte (tangencial) e que o avanço foi o parâmetro de corte de maior influência nas tensões residuais, na qualidade da superfície usinada e nas forças de usinagem. Notou-se que um melhor acabamento superficial pode ser obtido empregando-se ferramentas de corte wiper com avanços elevados.
In machining manufacturing processes, residual stresses arise from the combination of mechanical efforts from the tool/piece contact and the heat generated in the process. The cutting parameters, as well as the geometry of the cutting tools used, influence the achievement of different residual stress fields. Therefore, the turning process introduces residual stresses into the manufactured components, which depending on the nature, distribution and magnitude, can be beneficial or harmful, influencing the service life and fatigue resistance of the part. The present work aims to evaluate the influence of using different cutting tool geometries on residual stresses, cutting forces and surface integrity generated in the turning of SAE 1050 steel with cemented carbide tool inserts. The analysis of residual stresses was carried out by X-ray diffraction (XRD), using the sin²ψ method. Finally, an Artificial Neural Network (ANN) was developed, which demonstrated efficiency in predicting residual stresses and roughness for the studied material. The results indicated that turning generated residual stresses of greater magnitudes in the tangential direction to the feed of the cutting tool and that feed was the cutting parameter with the greatest influence on residual stresses, the quality of the machined surface and machining forces. It was noted that a better surface finish could be obtained using wiper cutting tools with high feeds.
In machining manufacturing processes, residual stresses arise from the combination of mechanical efforts from the tool/piece contact and the heat generated in the process. The cutting parameters, as well as the geometry of the cutting tools used, influence the achievement of different residual stress fields. Therefore, the turning process introduces residual stresses into the manufactured components, which depending on the nature, distribution and magnitude, can be beneficial or harmful, influencing the service life and fatigue resistance of the part. The present work aims to evaluate the influence of using different cutting tool geometries on residual stresses, cutting forces and surface integrity generated in the turning of SAE 1050 steel with cemented carbide tool inserts. The analysis of residual stresses was carried out by X-ray diffraction (XRD), using the sin²ψ method. Finally, an Artificial Neural Network (ANN) was developed, which demonstrated efficiency in predicting residual stresses and roughness for the studied material. The results indicated that turning generated residual stresses of greater magnitudes in the tangential direction to the feed of the cutting tool and that feed was the cutting parameter with the greatest influence on residual stresses, the quality of the machined surface and machining forces. It was noted that a better surface finish could be obtained using wiper cutting tools with high feeds.
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Organizado y patrocinado por: Federación iberoamericana de Ingeniería Mecánica y Universidad de Concepción - Chile. Departamento de Mecánica, FeIbIm – FeIbEM
Categorías UNESCO
Palabras clave
tensões residuais, torneamento, difração de raios-X, Rede Neural Artificial, residual stresses, turning; X-ray diffraction, Artificial Neural Network
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Centro
E.T.S. de Ingenieros Industriales
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Mecánica