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

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Now showing 1 - 10 of 25
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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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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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A numerical study of the effect of the thickness parameter on machining distortion for aluminum alloy plates

2021-08-04 , Rodríguez-Sánchez, Alejandro E. , Ledesma Orozco, Elías , Bárcenas, Leonardo

The deformation produced after the machining of a structural component is known as part distortion. This phenomenon is a consequence of the inherent residual stresses that exist in raw materials. In this study, such phenomenon is numerically investigated in simple plate elements by considering their thicknesses and their corresponding contribution to part distortion. A total number of eleven flat plates were analyzed using a numerical part distortion procedure for finite element models that also considered their machining positions. The results of this study show that part distortion has more impact on slender plates because these present higher loads than thicker plates in which the residual stresses self-balance throughout their section. Consequently, the part distortion phenomena in simple structural flat plates are related the plate thickness, their machining position, and geometrical parameters.

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A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

2021 , Rodríguez-Sánchez, Alejandro E. , Plascencia-Morán, Héctor

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

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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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An Artificial Neural Networks approach to predict low-velocity impact forces in an elastomer material

2020 , Rodríguez-Sánchez, Alejandro E.

The study of the impact phenomenon on rubber-like materials has been traditionally related to lumped parameter modeling or discrete Finite Element models that require experimentation associated with the material behavior at a level of constitutive modeling, and additional testing to validate their operation in case of engineering applications. This article presents an Artificial Neural Network approach to predict and simulate the low-velocity impact force in a thermoplastic elastomer material. Neural network models were trained and validated with experimental data obtained from impact tests in a modified Charpy apparatus. An experimental setup and a data acquisition procedure were set out to record the impact forces on elastomer specimens. The coefficient of determination R2, the Root Mean Square Error, and the Maximum Absolute Error measures were implemented as error functions to evaluate the performance of the neural networks regarding experimental data. Results show that the proposed method helps to predict and derive impact force curves within the range of the training data, since errors below 1% regarding experimental values were obtained. The results also demonstrate that the neural networks can simulate impact force curves within the range of the experimental values without the need to involve parameters of material strain-rate sensitivity. In addition, the approach was tested in another material, and the corresponding results show good prediction capabilities since errors below 1% were obtained. Therefore, it is concluded that the presented artificial neural models, and the approach, could be useful to create solution spaces for low-velocity impact responses of thermoplastic elastomers.

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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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Evidential neural network for tensile stress uncertainty quantification in thermoplastic elastomers

2024 , Rodríguez-Sánchez, Alejandro E.

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

2021 , Rodríguez-Sánchez, Alejandro E.

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