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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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Numerical Analysis of Machining Part Distortion in Aircraft Aluminum Structures

2020 , Ledesma-Orozco, Elías , Rodríguez-Sánchez, Alejandro E.

The inherent residual stresses in the raw materials of large monolithic structural components whereby machining procedures are needed to produce aircraft components, cause deviations, and distortions that are undesired and rise challenges for engineering design and engineering production teams of the aerospace companies. A numerical approach to address part distortion is proposed in this paper. An algorithm was developed and implemented as a finite element subroutine in the software ANSYS APDL, which uses the raw inherent residual stress parameters of the aluminum alloy and the machining locations of a structural specimen to simulate the machining distortion phenomenon in aircraft aluminum structures. This algorithm uses as inputs the finite element mesh of a component, the coefficients of residual stresses functions, and the machining location parameters from where a part is made of a raw material blank. The numerical results predicted the part distortion phenomenon with an Absolute Error of 2.79% with respect to initial experimental measurements of part distortion. Additionally, the proposed approach was used to develop part distortion curves by considering the machining location of the specimen. From these, numerical optimization techniques led to determine the machining location of the representative specimen that attained lower distortions. Such location corresponded to a vertical value around of 3.15 mm for the two simulated residual stresses conditions in the material. An additional measurement was carried out to validate the optimal numerical results and errors below 3% were obtained. Consequently, the proposed approach can be of use to determine, to reduce and to optimize part distortion without further experimental testing in structural aluminum 7050-T7451 alloy aircraft components.

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STRUCTURAL EFFECT OF STAGGERED COMPOSITE REINFORCEMENTS WITHIN THE FIREWALL OF AN AIRCRAFT

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

The performance of composite materials, such as high strength and low density, is ideal for aerospace applications. This paper presents the structural analysis of a component of a utility aircraft, the STELA M1. As this is still a prototype, the aim is to validate the resistance of its fuselage subjected first to static loads.

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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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Modeling hysteresis in expanded polystyrene foams under compressive loads using feed-forward neural networks

2023 , Rodríguez-Sánchez, Alejandro E. , Plascencia Mora, Héctor

Expanded polystyrene foams are widely used materials for various applications in engineering, including their use for protective designs. For this type of application, in engineering analysis and design, it is required to know the mechanical response to compression of this type of material, since energy parameters that support the analysis of the effectiveness of a design are derived from it. One of these parameters is strain hysteresis, through which it is possible to know how capable a material is of absorbing energy. The modeling and prediction of this parameter is a challenge from the analysis point of view. This contribution presents a method based on feed-forward artificial neural network models that address a modeling approach to derive this parameter from the mechanical response of expanded polystyrene foam. From this, models are constructed that can predict the response of such material to various density and loading rate conditions. The best of a total of 30 neural network models, which are capable of deriving energy parameters such as hysteresis, is chosen. The results show that this approach is valid for the deformation energy analysis of expanded polystyrene foams since results consistent with the material phenomenology and errors of less than 3% with respect to experimental data are obtained.

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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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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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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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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 energy absorption in expanded polystyrene foams

2019 , Rodríguez-Sánchez, Alejandro E. , Plascencia-Mora, Héctor , 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.