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Flexural strength prediction of randomly oriented chopped glass fiber composite laminate using artificial neural network

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Abstract

Randomly oriented chopped glass fiber reinforced polymer (ROCGFRP) composite laminate exhibits better flexural behavior, intended to use in various industrial applications such as aerospace, automobile, defence and marine engineering industries. This work employs the artificial neural network (ANN) model to predict the flexural strength of the randomly oriented chopped glass fiber composite beam using MATLAB\(^{\circledR }\)2021a. For this purpose, experimental flexural strength test based on ASTM D790 and finite element analysis (FEA) simulation with cohesive zone model using ANSYS Workbench 19.2 were conducted to compute the input and output parameters for the ANN model. Strain and stress datasets were chosen as the model’s input and output parameters, respectively. The entire datasets i.e., seven thousand six hundred and sixteen points, were divided into training, validation and test sets in the proportion of 70:15:15, respectively. The appropriate structure of the ANN model such as input layer, hidden layer, output layer, activation functions and training algorithm are selected and evaluated using statistical tools. The number of neurons in the hidden layer is optimized using Levenberg-Marquardt training algorithm. The training, validation and test components are fitted along the regression line, which shows strong relationship between analysis and desired outcomes. Thus, it is observed that, there is a high level of consistency is observed among the numerical, experimental and predicted results. Finally, the obtained ANN model is good predictor for flexural strength of ROCGFRP composite laminate.

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Abbreviations

ANN:

Artificial neural network

BP:

Back-propagation

CZM:

Cohesive zone method

FE:

Finite element

FEA:

Finite element analysis

GFRP:

Glass fiber reinforced polymer

GRNN:

Generalised regression neural network

L–M:

Levenberg–Marquardt

PVA:

Poly-vinyl alcohol

PLA:

Polylactic acid

QN:

Quasi Newton

RBP:

Resilient back propagation

RBFNN:

Radial basis function neural network

RMS:

Root mean square

ROCGFRP:

Randomly oriented chopped glass fiber reinforced polymer

SVM:

Support vector machine

UTM:

Universal testing machine

VLRBP:

Variable learning rate back propagation

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli, 620015, Tamil Nadu, India

    Pankaj Chaupal & Prakash Rajendran

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  1. Pankaj Chaupal
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  2. Prakash Rajendran
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Correspondence to Prakash Rajendran.

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Chaupal, P., Rajendran, P. Flexural strength prediction of randomly oriented chopped glass fiber composite laminate using artificial neural network. J Braz. Soc. Mech. Sci. Eng. 45, 131 (2023). https://doi.org/10.1007/s40430-023-04061-9

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