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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57202719691

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Application of artificial neural networks to map the mechanical response of a thermoplastic elastomer

2019 , Rodríguez-Sánchez, Alejandro E. , Elías Ledesma-Orozco , Sergio Ledesma , Agustín Vidal-Lesso

Thermoplastic elastomers are materials widely used in engineering applications due to their excellent performance to absorb mechanical vibrations and to reduce impact forces. However, their mechanical response is non-linear, which prevents linear models from predicting stresses reliably in the design and analyses of mechanical parts. This study presents a feedforward artificial neural network that was trained with stress/strain data of a thermoplastic elastomer. Such data come from a database specialized in materials from which ten curves were obtained to train and to develop an artificial neural network model. Additionally, five hyperelastic models and two probabilistic neural networks were used and compared to the proposed model. The simulation results show that the feedforward artificial neural network model is the most accurate to predict the non-linear thermoplastic elastomer response because it presented a coefficient of determination (R2) of 0.996 0 and differences of 1% with respect to the experimental data. The artificial neural network model also served to map the stress response for a temperature range between −20 °C to 160 °C for the thermoplastic elastomer material. On this basis, the presented feedforward neural network approach was tested by predicting the response of seven additional thermoplastic elastomers. The results showed that such an approach can attain thermoplastic elastomers responses with differences of 4% respect to experimental data. Consequently, the proposed approach simplifies the prediction of stress/strain curves for thermoplastic elastomer materials.

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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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The use of neural networks and nonlinear finite element models to simulate the temperature-dependent stress response of thermoplastic elastomers

2019 , Rodríguez-Sánchez, Alejandro E. , Sergio Ledesma , Vidal-Lesso, José Agustín , Elias Ledesma-orozco

In this study, a methodology that combines artificial neural networks and nonlinear hyperelastic finite element modeling to simulate the temperature-dependent stress response of elastomer solids is presented. The methodology is verified by a discrete model of a tensile test specimen, which is used to generate stress–strain pairs of existent experimental data. The proposed method is also tested with a benchmark problem of a rubber-like cylinder under compression. Three grades of an elastomer used for diverse engineering applications are used throughout the study. On this basis, three neural network architecture with 10 hidden neurons are implemented as constitutive models to reproduce the experimental data of the materials. The validation results show that the proposed methodology can reproduce tensile tests with an error of 5% of less than regarding experimental data for elastomers that present no yielding point. The benchmark problem results were at the range expected for the elastomer materials with no yielding, where it was possible to derive force temperature-dependent responses. These results suggest that the methodology helps the prediction of the material response when only material stress–strain curves at different temperatures exist. Therefore, the presented approach in this contribution helps to simulate the temperature-dependent stress responses of elastomeric solids with no defined yielding point.

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