Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Breuer, Kevin; Stommel, Markus

doi:10.3390/fib9020008

Cited by 43 publications

(20 citation statements)

References 27 publications

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“…Breuer et al applied ANN for short fiber composites under representative volume element database. The elastic properties of short fiber reinforced plastics were evaluated and results showed that ANN predicts the stiffness in good manner 18 . Wang et al used ANN to predict the tensile strength of ultrafine glass fiber felts.…”

Section: Introductionmentioning

confidence: 99%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

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Section: Introductionmentioning

confidence: 99%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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show abstract

“…The results showed that ANN model outperformed MLR in the prediction accuracy of elongation at break and breaking strength. Breuer et al used ANN to predict the short fiber composite properties using RVE database [ 22 ]. The prediction of the elastic properties of short fiber reinforced plastics by ANN has been compared with additional finite element results.…”

Section: Introductionmentioning

confidence: 99%

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

et al. 2022

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In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

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“…Similarly, an ANN is trained to predict the elastic properties of short fiber reinforced plastics in [52].…”

Section: Data-based Multiscale Modeling and Simulationmentioning

confidence: 99%

FE${}^\textbf{ANN}$ $-$ An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

Kalina¹,

Linden²,

Brummund³

et al. 2022

Preprint

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Herein, we present a new data-driven multiscale framework called FE ANN which is based on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) as macroscopic surrogate models and an autonomous data mining process. Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale. Thereby, we restrict ourselves to finite strain hyperelasticity problems for now. By using a set of problem specific invariants as the input of the ANN and the Helmholtz free energy density as the output, several physical principles, e. g., objectivity, material symmetry, compatibility with the balance of angular momentum and thermodynamic consistency are fulfilled a priori. The necessary data for the training of the ANN-based surrogate model, i. e., macroscopic deformations and corresponding stresses, are collected via computational homogenization of representative volume elements (RVEs). Thereby, the core feature of the approach is given by a completely autonomous mining of the required data set within an overall loop. In each iteration of the loop, new data are generated by gathering the macroscopic deformation states from the macroscopic finite element (FE) simulation and a subsequently sorting by using the anisotropy class of the considered material. Finally, all unknown deformations are prescribed in the RVE simulation to get the corresponding stresses and thus to extend the data set. The proposed framework consequently allows to reduce the number of time-consuming microscale simulations to a minimum. It is exemplarily applied to several descriptive examples, where a fiber reinforced composite with a highly nonlinear Ogden-type behavior of the individual components is considered. Thereby, a rather high accuracy could be proved by a validation of the approach.

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Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Cited by 43 publications

References 27 publications

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

FE${}^\textbf{ANN}$ $-$ An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining

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