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Optimización de la configuración de materiales compuestos laminados mediante redes neuronales y templado simulado
(CULCyT: Cultura Científica y Tecnológica, 2024)
Elias Ledesma-orozco
;
Rodríguez-Sánchez, Alejandro E.
;
Julio César Galvis Chacón
La optimización de materiales compuestos laminados es uno de los principales desafíos en el diseño de componentes o sistemas estructurales debido a la influencia de múltiples parámetros en su desempeño y respuesta mecánica ante la deformación. Esta investigación utiliza un metamodelo basado en redes neuronales artificiales para predecir índices de desempeño, específicamente el índice de falla de Tsai-Wu, a partir de la configuración del laminado de un material compuesto sujeto a cargas considerando espesores y orientaciones de sus fibras. El metamodelo alimenta una función objetivo diseñada para mejorar la configuración de una pieza mediante la optimización de las orientaciones de las fibras. Se combina un algoritmo de templado simulado adaptado para materiales compuestos laminados con redes neuronales, generando un espacio de soluciones que ofrece al diseñador una amplia gama de opciones para abordar el análisis del problema. El método reportado es una alternativa eficiente al método tradicional de análisis de materiales compuestos laminados, agilizando el proceso y ampliando las posibilidades de configuración disponibles para su selección.
9
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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
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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.
Scopus© Citations 6 7
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Numerical analysis of energy absorption in expanded polystyrene foams
(2019)
Rodríguez-Sánchez, Alejandro E.
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Plascencia-Mora, Héctor
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Elias Ledesma-orozco
;
Eduardo Aguilera-gomez
;
Gómez-Márquez, Diego A.
The expanded polystyrene foam is widely used as a protective material in engineering applications where energy absorption is critical for the reduction of harmful dynamic loads. However, to design reliable protective components, it is necessary to predict its nonlinear stress response with a good approximation, which makes it possible to know from the engineering design analysis the amount of energy that a product may absorb. In this work, the hyperfoam constitutive material model was used in a finite element model to approximate the mechanical response of an expanded polystyrene foam of three different densities. Additionally, an experimental procedure was performed to obtain the response of the material at three loading rates. The experimental results show that higher densities at high loading rates allow better energy absorption in the expanded polystyrene. As for the energy dissipation, high dissipation is obtained at higher densities at low loading rates. In the numerical results, the proposed finite element model presented a good performance since root mean square error values below 9% were obtained around the experimental compressive stress/strain curves for all tested material densities. Also, the prediction of energy absorption with the proposed model was around a maximum error of 5% regarding the experimental results. Therefore, the prediction of energy absorption and the compressive stress response of expanded polystyrene foams can be studied using the proposed finite element model in combination with the hyperfoam material model.
Scopus© Citations 16 13
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Part distortion optimization of aluminum-based aircraft structures using finite element modeling and artificial neural networks
(CIRP Journal of Manufacturing Science and Technology, 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.
Scopus© Citations 15 25

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