Artificial intelligence techniques in simulation of viscoplasticity of polymeric composites

Al-Haik, Marwan; Hussaini, M. Y.; Rogan, C.

doi:10.1002/pc.20745

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Each bias b j of hidden neuron j represents a constant external input with a value of 1 multiplied by its weight w ji , which is the appropriate element of the input weight matrix of hidden layer IW 1,1 with the size S 1 x R , and is the primary activity level of the hidden neuron j . The vector n 1 with the length S 1 , whose elements n j represent the distances between the multiplied inputs p i and their related weights w ji , serves as the net input n 1 of the hidden layer, or as the activation potentials [24] of its RBF neurons.…”

Section: Methodsmentioning

confidence: 99%

“…It was designed to study the behavior of the real, non-linear, static-dynamic, complex systems by computer simulation via the recognition, classification and generalization of learned patterns. A structure of the ANN’s is composed of a large number of highly interconnected and mutually interacting processing elements (artificial neurons) organized in layers analogous to biological neurons that are tied together with weighted connections (synaptic weights) analogous to biological synapses [24]. Each ANN consists of at least three layers – one input layer, at least one hidden layer, and one output layer.…”

Section: Introductionmentioning

confidence: 99%

“…The transfer function of hidden RBF neurons is an attenuation, central-radial symmetry, non-negative, non-linear function. All functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function [24]. Usually, the hidden layer neurons include the non-linear RBF Gaussian function, and a non-linear sigmoid or linear function is located in output layer neurons as its transfer (activation) function.…”

Section: Introductionmentioning

confidence: 99%

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Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

et al. 2019

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The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

et al. 2019

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“…The MATLAB computer program was used to find the optimal number of neurons and the width (w) described in Section 2.7. The objective function was to minimize MSE [34].…”

Section: Analysis Of the Radial Basis Function Network (Rbfn) Modelmentioning

confidence: 99%

Use of Radial Basis Function Network to Predict Optimum Calcium and Magnesium Levels in Seawater and Application of Pretreated Seawater by Biomineralization as Crucial Tools to Improve Copper Tailings Flocculation

et al. 2020

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The combined use of the Radial Basis Function Network (RBFN) model with pretreated seawater by biomineralization (BSw) was investigated as an approach to improve copper tailings flocculation for mining purposes. The RBFN was used to set the optimal ranges of Ca2+ and Mg2+ concentration at different Ph in artificial seawater to optimize the performance of the mine tailings sedimentation process. The RBFN was developed by considering Ca2+ and Mg2+ concentration as well as pH as input variables, and mine tailings settling rate (Sr) and residual water turbidity (T) as output variables. The optimal ranges of Ca2+ and Mg2+ concentration were found, respectively: (i) 169–338 and 0–130 mg·L−1 at pH 9.3; (ii) 0–21 and 400–741 mg·L–1 at pH 10.5; (iii) 377–418 and 703–849 mg·L−1 at pH 11.5. The settling performance predicted by the RBFN was compared with that measured in raw seawater (Sw), chemically pretreated seawater (CHSw), BSw, and tap water (Tw). The results highlighted that the RBFN model is greatly useful to predict the settling performance in CHSw. On the other hand, the highest Sr values (i.e., 5.4, 5.7, and 5.4 m·h–1) were reached independently of pH when BSw was used as a separation medium for the sedimentation process.

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“…Genetic algorithm‐based strategies were used to prune the proposed neural network. In comparison, the neuro computational model has been found to outperform the phenomenological model .…”

Section: Introductionmentioning

confidence: 99%

Viscoplastic parameter identification of temperature‐dependent mechanical behavior of modified polyphenylene oxide polymers

Doh

Hur

Lee

2018

Polymer Engineering & Sci

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Modified polyphenylene oxide (MPPO) is a widely known thermoplastic polymer and is extensively utilized for weight reduction of automobiles since it possesses outstanding mechanical properties, such as resistance and toughness. In this study, the viscoplastic behaviors of MPPO with respect to changes in temperature were identified through an approximate optimization method. For this work, parameter studies were conducted with a tensile simulation of the MPPO polymer, in accordance with the modified two‐layer viscoplastic material parameters and the suggested shift parameter. The sensitivity tendency of the force‐displacement curves was captured in accordance with the material parameters. Computational experiments for obtaining calibration error data were performed using the material parameters based on interior central composite design. Surrogate models of root mean square error relative to force and displacement were generated using the response surface method. The accuracy of the surrogate models was evaluated with R‐square. Optimization for obtaining the material parameters was performed using the non‐dominant sorting genetic algorithm‐II. The results showed that the calibration error from the finite element analysis was minimized and agreed with experimental data. POLYM. ENG. SCI., 59:E200–E211, 2019. © 2018 Society of Plastics Engineers

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Artificial intelligence techniques in simulation of viscoplasticity of polymeric composites

Cited by 10 publications

References 14 publications

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems

Use of Radial Basis Function Network to Predict Optimum Calcium and Magnesium Levels in Seawater and Application of Pretreated Seawater by Biomineralization as Crucial Tools to Improve Copper Tailings Flocculation

Viscoplastic parameter identification of temperature‐dependent mechanical behavior of modified polyphenylene oxide polymers

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