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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Evidential neural network for tensile stress uncertainty quantification in thermoplastic elastomers

2024 , Rodríguez-Sánchez, Alejandro E.

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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.

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Neural networks-based modeling of compressive stress in expanded polystyrene foams: A focus on bead size parameters

2024 , Pech-Mendoza, Héctor , Rodríguez-Sánchez, Alejandro E. , Hector Plascencia Mora

Expanded polystyrene is used in diverse applications, notably for protective and structural purposes. Its cushioning and mechanical strength excel under compressive loads, especially when optimally designed. A key factor influencing its compressive stress is the initial density, which plays a significant role in determining the material’s mechanical properties. This aspect is primarily determined by the bead size distribution. Although there is a vast body of literature on modeling the stress response of expanded polystyrene, there is limited emphasis on predictions that account for this factor, which is also relevant for the manufacturing of the material. Recent literature has emphasized the capability of artificial neural networks in predicting the compressive behaviors of expanded polystyrene, incorporating various factors. In this study, artificial neural network models were used to predict the compressive stress responses of polystyrene foams, with a focus on bead size distribution parameters. Specimens of two distinct initial densities were examined using micrographs to identify bead diameters and distributions, which were then used as model inputs. Compression tests on these specimens were conducted at two different rates. The collected data facilitated the development of predictive models for the material’s compressive behavior. The model predictions closely match experimental findings, with error metrics showing deviations compared to the experimental data. This highlights the utility of artificial neural networks in modeling the compressive behavior of polystyrene foams, particularly when bead size and related parameters are considered.

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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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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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Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

2024 , Rodríguez-Sánchez, Alejandro E. , Acevedo, Mario

Abstract This study addresses the challenge of modeling compressive stress in AlSi10Mg composites by introducing a method that employs feedforward artificial neural networks (ANNs) and their interpretability, which helps to simulate and analyze material behavior under various conditions. The main objective is to develop a predictive ANN model that can effectively simulate material responses under several factors, incorporating diverse testing parameters and material specifications related with its synthesis. An optimized ANN model, featuring eleven neurons in its hidden layer, was used and demonstrated high predictive accuracy, achieving R 2 values exceeding 0.94. Additionally, a SHAP interpretability analysis was conducted to assess the influence of key factors such as strain and material conditions on the stress response. The results highlight the significant role of material synthesis processes, compared to the strain rate, in the stress response. In conclusion, this method presents a comprehensive tool for studying complex stress behaviors in AlSi10Mg-based composites , offering insights that could guide future material development and research.

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An optimization of part distortion for a structural aircraft wing rib: an industrial workflow approach

2020 , Leonardo Barcenas , Elias Ledesma-Orozco , Sjoerd Van-der-Veen , Francisco Reveles-Arredondo , Rodríguez-Sánchez, Alejandro E.

Inherent residual stresses in the raw material of large monolithic structural components cause distortion when parts are machined. This is a frequent problem in the manufacturing life cycle of aircraft parts that has resulted in recurring concession, rework or scrap, which cost millions of euros to the aerospace industry. In this study an industrial workflow was implemented to predict and optimize part distortion after the machining of a representative structural aircraft-component. This was carried out by means of the following steps: i) characterization of inherent residual stresses of a material using a modified layer removal method; ii) generation of residual stress profiles using part-distortion measurements; and iii) optimization of distortion using Finite Element Analysis to derive an optimal part location. The workflow was validated with three experiments from two aluminum 7050-T7451 specimens with different residual stress states. A prediction of distortion with a Finite Element model resulted in 0.44 mm from the higher residual stresses while from the lower residual stresses the maximum distortion was 0.36 mm. The experimental testing was carried out in a CNC center to validate the numerical results. Good agreement from the numerical results in regards experimental tests led to propose the optimal part location which decreased the distortion up to 0.12 mm.

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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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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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