Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

Hou, Junling; Li, Xuan; Zhang, Kaining; Jing, Yidong; Zhang, Zhenjie; You, J. F.; Li, Qun

doi:10.3390/ma15113776

Cited by 13 publications

(6 citation statements)

References 43 publications

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“…The observed residual errors in the hybrid numerical-experimental identification process hence were fully consistent with the level of uncertainty normally entailed by FEMU-based characterization. Optically measured (d) Remarkably, HS-JAYA solved variants 1 and 2 of the identification problem including, respectively, 324 and 110 unknown material/structural parameters, a significantly larger number of unknowns than those usually reported in the literature for elastomers and rubberlike materials [66][67][68][69][70][71][72] (from two to six unknown material parameters or neural networks depending on three input characteristics), visco-hyperelastic materials [73][74][75] (from six to nine unknown material parameters including tangent modulus and softening index, Prony constants and relaxation times), biological tissues [76][77][78][79][80][81] (from five to sixteen unknowns accounting also for visco-elastic effects and stochastic variation of material properties), non-homogeneous hyperelastic structures [37,63,[82][83][84] (from four to sixteen unknown material parameters for the global model, or two unknown material parameters for each local inverse problem at the element level) or anisotropic hyperelastic materials modeled with much more complicated constitutive equations [27,34,[84][85][86] (from three to seventeen unknown material parameters). A very recent study by Borzeszkowski et al [38] considered identification problems of nonlinear shells subject to various loading conditions (i.e., uniaxial tension, pure bending, sheet inflation and abdominal wall pressurization).…”

Section: Solution Of the Inverse Problem: Fe Analysis And Metaheurist...mentioning

confidence: 99%

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

et al. 2023

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Mechanical characterization of soft materials is a complicated inverse problem that includes nonlinear constitutive behavior and large deformations. A further complication is introduced by the structural inhomogeneity of tested specimens (for example, caused by thickness variations). Optical methods are very useful in mechanical characterization of soft matter, as they provide accurate full-field information on displacements, strains and stresses regardless of the magnitude and/or gradients of those quantities. In view of this, the present study describes a novel hybrid framework for mechanical characterization of soft membranes, combining (i) inflation tests and preliminary in-plane equi-biaxial tests, (ii) a one-shot projection moiré optical setup with two symmetric projectors that project cross-gratings onto the inflated membrane, (iii) a mathematical model to extract 3D displacement information from moiré measurements, and (iv) metaheuristic optimization hybridizing harmony search and JAYA algorithms. The use of cross-gratings allows us to determine the surface curvature and precisely reconstruct the shape of the deformed object. Enriching metaheuristic optimization with gradient information and elitist strategies significantly reduces the computational cost of the identification process. The feasibility of the proposed approach wassuccessfully tested on a 100 mm diameter natural rubber membrane that had some degree of anisotropy in mechanical response because of its inhomogeneous thickness distribution. Remarkably, up to 324 hyperelastic constants and thickness parameters can be precisely identified by the proposed framework, reducing computational effort from 15% to 70% with respect to other inverse methods.

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Section: Solution Of the Inverse Problem: Fe Analysis And Metaheurist...mentioning

confidence: 99%

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

et al. 2023

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“…A trial-and-error method was, for example, used to determine the spread constant σ of the GRNN model to predict the curing characteristics of RBs in our earlier work [7]. A cross-validation method for the GRNN optimisation was employed in article [31], which was dedicated to the identification of material parameters in the constitutive model of hyper-elastic materials such as rubber. A hold-out method of selecting the optimal value of σ was proposed in [22].…”

Section: Generalised Regression Neural Network Theorymentioning

confidence: 99%

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Kopal,

Labaj,

Vršková

et al. 2023

Polymers

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Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity–time curves, acquired by a rubber process analyser for styrene–butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

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“…GRNN Model. In 1991, Specht [26] proposed generalized regression neural network (GRNN), which is a special form of radial basis function neural network (RBF) [27].…”

Section: Ifoa-grnn Model Designmentioning

confidence: 99%

Movie Box Office Prediction Based on IFOA‐GRNN

Zhang

Zhan

2022

Discrete Dynamics in Nature and Society

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Predicting movie box office has received extensive attention from academia and industry. At present, the main method of forecasting movie box office is subjective prediction, which is not widely accepted due to its accuracy and applicability. This study improves the fruit fly algorithm to optimize the generalized regression neural network (IFOA-GRNN) model to predict whether a movie can become a high-grossing movie. By using the actual box office data and performing virtual simulation calculations, the root means square error of the IFOA-GRNN model predicting the movie box office is 0.3412, and the classification accuracy is about 90%. By comparing this model with FOA-GRNN, KNN, GRNN, Random Forest, Naive Bayes, Ensembles for Boosting, Discriminant Analysis Classifier, and SVM, it is found that the prediction effect of the IFOA-GRNN model is significantly better than the above eight models. The contribution of this article is to propose a generalized regression neural network model based on an improved fruit fly optimization algorithm, which can greatly improve the accuracy of movie box office prediction.

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Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

Cited by 13 publications

References 43 publications

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

Movie Box Office Prediction Based on IFOA‐GRNN

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