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

Rodríguez-Sánchez, Alejandro E

doi:10.1021/bk-2022-1416.ch005

Cited by 4 publications

(18 citation statements)

References 24 publications

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“…For the case of energy absorption, Figure 7 shows that the best ANN model can estimate testing data with low error predictions (below 3%); in the same manner, both strain energy and efficiency parameter were predicted accurately. However, what constitutes an improvement of ANN models reported in this work regarding previous studies, 13,14,39 is that the current can predict hysteresis data as is presented in Figure 13. As is well known, hysteresis is one of the most important parameters to evaluate the absorption and dissipation capabilities of kinetic energy during compressive loadings in polymer foams, such as those that occur in impact events.…”

Section: Discussionmentioning

confidence: 55%

“…Thus, a convenient way to compute these parameters using neural networks is to address the stress response and then derive each of them from it using Equations (2)–(5). Such an approach has been successfully proven for numerical methods in the past giving low error regarding experimental data (see previous related studies 13,14 ). Thus, the compressive stress response σ c and the characteristics of a foam define a dataset S from which a neural-network-based model is trained and can be described as follows:where ρ i refers to the density of the polymer foam, V i correspond to the loading rate of the applied load, s i ∈ {0, 1} is a switch characteristic that relates the state of deformation of the foam during testing: 1 is used for the loading phase and 0 for the unloading phase.…”

Section: Methodsmentioning

confidence: 97%

“…The overall method to construct a model is presented in Figure 3, which has been proved to be useful in a previous contribution for the modeling of compressive stress responses of an EPP foam and which comprehends a Machine Learning pipeline for such purpose. 14 In this case, no model deployment is carry out, and the machine learning pipeline describes in the following section.

Figure 3.Main method.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, a convenient way to compute these parameters using neural networks is to address the stress response and then derive each of them from it using Equations ( 2)- (5). Such an approach has been successfully proven for numerical methods in the past giving low error regarding experimental data (see previous related studies 13,14 ). Thus, the compressive stress response σ c and the characteristics of a foam define a dataset S from which a neural-network-based model is trained and can be described as follows:…”

Section: Neural-network-based Hysteresis Modeling In Polymer Foamsmentioning

confidence: 99%

“…On this point, recent advancements using artificial neural networks have been applied to foams for their mechanical modeling. [13][14][15] These artificial-neural-network-based computational models effectively can produce synthetic data to come across those challenges presented in traditional constitutive models. As such, these advancements represent a new strategy to understand further energy-related parameters in polymer foams and, specifically, in polystyrene foam variants, because it is well-known that such properties can be directly derived from stress-strain-related data in foam materials.…”

Section: Introductionmentioning

confidence: 99%

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

Rodríguez-Sánchez

Mora

2023

Journal of Cellular Plastics

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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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Section: Discussionmentioning

confidence: 55%

Section: Methodsmentioning

confidence: 97%

Figure 3.Main method.…”

Section: Methodsmentioning

confidence: 99%

Section: Neural-network-based Hysteresis Modeling In Polymer Foamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rodríguez-Sánchez

Mora

2023

Journal of Cellular Plastics

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

Rodríguez-Sánchez,

Acevedo-Alvarado

2024

Eng. Res. Express

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This study addresses the challenge of modeling compressive stress in AlSi10Mg foams 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 foams, offering insights that could guide future material development and research.

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

Rodríguez-Sánchez,

Plascencia-Mora,

Acevedo-Alvarado

2024

Journal of Cellular Plastics

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

Cited by 4 publications

References 24 publications

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

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

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

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

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