Rodríguez-Sánchez, Alejandro E.
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Rodríguez-Sánchez, Alejandro E.
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Rodríguez-Sánchez, Alejandro Esteban
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Rodríguez-Sánchez, Alejandro E.
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Scopus Author ID
57202719691

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A numerical study of the effect of the thickness parameter on machining distortion for aluminum alloy plates

2021-08-04 , Rodríguez-Sánchez, Alejandro E. , Ledesma Orozco, Elías , Bárcenas, Leonardo

The deformation produced after the machining of a structural component is known as part distortion. This phenomenon is a consequence of the inherent residual stresses that exist in raw materials. In this study, such phenomenon is numerically investigated in simple plate elements by considering their thicknesses and their corresponding contribution to part distortion. A total number of eleven flat plates were analyzed using a numerical part distortion procedure for finite element models that also considered their machining positions. The results of this study show that part distortion has more impact on slender plates because these present higher loads than thicker plates in which the residual stresses self-balance throughout their section. Consequently, the part distortion phenomena in simple structural flat plates are related the plate thickness, their machining position, and geometrical parameters.

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Part distortion optimization of aluminum-based aircraft structures using finite element modeling and artificial neural networks

2020 , Rodríguez-Sánchez, Alejandro E. , Elias Ledesma-orozco , Sergio Ledesma

Currently, in the aircraft design, thinner structures are required to reduce weight, which in turn presents challenges for the manufacturing of parts and components. One of the identified problems in manufacturing is the machining distortion phenomenon, which causes the generation of scrap during the production of mechanical and structural components. This study presents the use of a finite element procedure, artificial neural network models, and the simulated annealing algorithm to optimize machining distortion phenomena in aluminum-based structures. A finite element procedure that simulates machining distortion by considering residual stresses and machining locations is used to generate training and validation data sets for the construction of an artificial neural network model. Once the performance of the artificial neural network is validated, simulated annealing is used in combination with the neural network model to find the optimum parameters of the machining locations and the residual stresses conditions that reduce distortion phenomena caused by machining. A case study of a specimen that has complex geometrical features, such as those that present in the design of aircraft structures, was used for the validation of the models. The results show that the proposed approach predicts the machining distortion of the specimen obtaining errors below 3% regarding experimental observations. Numerical results not only predict maximum distortions, but the evidence shows that the finite element can estimate the distribution of the distortion presented experimentally in the case study. Additionally, the optimization results helped to reduce the distortions 80% or more for high levels of deformation. Therefore, the proposed method in this study helps in the prediction and optimization of machining distortion of aluminum-based structures.

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