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

Research Output
Now showing 1 - 10 of 22
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Evidential neural network for tensile stress uncertainty quantification in thermoplastic elastomers

2024 , Rodríguez-Sánchez, Alejandro E.

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Modeling Nonlinear Compressive Stress Responses in Closed-Cell Polymer Foams Using Artificial Neural Networks: A Comprehensive Case Study

2022-06-14 , Rodríguez-Sánchez, Alejandro E.

Closed-cell polymer foams are versatile materials that have multiple applications in several industries due to their mechanical energy absorption capabilities, and they are used to design protective devices as engineering solutions or to produce packaging solutions to protect goods. Because most of these products and devices operate in compression, it is necessary to know the material behavior to compressive loadings for modeling and design purposes. In this work, the compressive response of a closed-cell expanded polypropylene polymer foam is modeled using feed-forward artificial neural network models as a case study. Practical considerations and a methodology that includes the basic steps to conduct the modeling of the compressive stress of foams of this class are presented. The modeling uses foam densities, loading rates, and strain as inputs in an artificial neural network system to model compressive stress in the foam. Results help conclude that feed-forward neural networks can model the response at compression for expanded polypropylene foams considering different variables since prediction errors close to the unity of the coefficient of determination R2 and error results below 2% regarding testing data were obtained.

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Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

2024 , Elías Ledesma-Orozco , Julio C. Galvis-Chacón , Rodríguez-Sánchez, Alejandro E.

In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.

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ANÁLISIS ESTRUCTURAL DE UN CORTAFUEGOS CON REFUERZOS INTERNOS ESCALONADOS CON COMPUESTOS DE UN AVIÓN UTILITARIO

2019 , Victor Alfonso Ramirez Elías , Rodríguez-Sánchez, Alejandro E. , ELIAS RIGOBERTO LEDESMA OROZCO , HILARIO HERNANDEZ MORENO , ERIK VARGAS ROJAS , LUIS FERNANDO PUENTE MEDELLIN , LUIS DAVID CELAYA GARCIA

With the aim to validate the strength of one of the most critical areas of the utilitarian aircraft Stela M1, this work shows a structural analysis of the firewall with inner stepped reinforces “Doublers”, which it is subjected to maximum static loads from typical flight manoeuvres suggested by the standard FAR 23, specifically for assessing the interlaminar effects of the stepped reinforces. The piece, including the stepped reinforces, is made totally with laminated composite materials carbon fibre/epoxy and protected with a thin aluminium plate. It was simulated with a finite element model and validated with experimental data from a representative specimen subjected to the same operational conditions. From the results, it was observed, throughout the cross-section of the piece, that the first layers and the plate have a significant effect on the X and Y stresses, whereas the effect of the stepped reinforces at the layers near to the mid-plane were more evident for the XY shear stresses. Complementary, the highest Tsai-Wu index were located around the screw hole C and at the first carbon fibre layers and suggest a security factor of around 7, validating the piece. This study also shows that the stress concentrations around the stepped reinforces barely have a deleterious effect on the firewall. Moreover, the reinforces are suitable to significantly increase the structure strength and to keep the maximum stresses concentrated at the firewall reinforced areas.

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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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Numerical analysis of wood-high-density polyethylene composites: A hyperelastic approach

2018 , Rodríguez-Sánchez, Alejandro E. , Alejandro Vega-Rios , Mónica E Mendoza-Duarte , Sergio G Flores-Gallardo , E Armando Zaragoza-Contreras

The application of a hyperelastic approach to simulate the tensile mechanical behavior of wood fiber/polymer composites is proposed. This research was conducted with the purpose of selecting the theoretical model that best fits the experimental data for use in the finite element model. The analyses by the four strain energy density functions (Polynomial, Ogden, Yeoh, and Marlow models) and the Cauchy-Green tensor invariants were used as the theoretical models. The experimental mechanical behavior of three wood fiber/polymer composites formulated with high-density polyethylene as the polymer matrix, and pine, cherry, and walnut sawdust as the fillers, at a concentration of 40 wt%, was evaluated. Experimental data showed that with filler addition, the tensile modulus of the high-density polyethylene matrix increased almost 131% regarding the neat high-density polyethylene; however, no significant differences were found respecting the kind of sawdust. Nevertheless, it was found that the elongation (%) at break was higher when walnut sawdust was employed. As for the strain energy density function analyses, the best approximation to the experimental data was achieved by the Marlow model, because this model only demands the sum of the principal extension ratios for a polymer-based material, I1. The numerical results showed that the proposed finite element model predicts the response with less than 1% error, regarding the experimental data, and consequently the use of the finite element models was simplified for the prediction of the tensile mechanical behavior of this kind of composites.

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Neural network-driven interpretability analysis for evaluating compressive stress in polymer foams

2024 , Rodríguez-Sánchez, Alejandro E. , Héctor Plascencia-Mora , Acevedo, Mario

This research presents a method to analyze how neural network models, applied to Expanded Polypropylene and Expanded Polystyrene foams, predict their compressive stress responses. By using SHAP values and Partial Dependence Plots, the study elucidates the models’ decision-making processes. It focuses on three main features for both materials: density, loading rate, and strain, with an additional feature concerning loading and unloading for Expanded Polystyrene foam. The findings highlight that increased density and loading rate are closely correlated with higher compressive responses, and strain emerges as the most influential factor for the response of both materials. Partial Dependence Plots reveal a linear relationship with density, whereas other variables demonstrate non-linear relationships. These results validate the use of neural networks in analyzing material behavior, showing that the models’ outputs are in line with empirical observations. In conclusion, as presented, the integration of interpretability tools with neural network models offers a robust method for material response analysis, contributing to a deeper understanding of material science.

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A neural networks metamodeling approach to address part distortion in aircraft aluminum structures

2021 , Rodríguez-Sánchez, Alejandro E.

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La inteligencia artificial y sus modelos de redes neuronales

2024-01-22 , Rodríguez-Sánchez, Alejandro E.

Este artículo de divulgación revisa qué son los modelos en la inteligencia artificial (IA), con especial énfasis en las redes neuronales artificiales y su capacidad para simular y predecir fenómenos complejos. Ejemplifica la apli- cación multidisciplinaria de la IA en campos como la astronomía, destacandola imagen del primer agujero negro, y en biología molecular, con los avances de AlphaFold. Se resalta la necesidad de entender los modelos de IA más allá de su función técnica, subrayando su contribución al progreso científico. Concluye que la IA, a través de sus modelos, desempeña un papel crucial en el estudio de las regularidades de la naturaleza y de la sociedad.

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Neural network-driven interpretability analysis for evaluating compressive stress in polymer foams

2024 , Rodríguez-Sánchez, Alejandro E. , Plascencia Mora, Héctor , Acevedo, Mario

This research presents a method to analyze how neural network models, applied to Expanded Polypropylene and Expanded Polystyrene foams, predict their compressive stress responses. By using SHAP values and Partial Dependence Plots, the study elucidates the models’ decision-making processes. It focuses on three main features for both materials: density, loading rate, and strain, with an additional feature concerning loading and unloading for Expanded Polystyrene foam. The findings highlight that increased density and loading rate are closely correlated with higher compressive responses, and strain emerges as the most influential factor for the response of both materials. Partial Dependence Plots reveal a linear relationship with density, whereas other variables demonstrate non-linear relationships. These results validate the use of neural networks in analyzing material behavior, showing that the models’ outputs are in line with empirical observations. In conclusion, as presented, the integration of interpretability tools with neural network models offers a robust method for material response analysis, contributing to a deeper understanding of material science.

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