Identification of failure modes in GFRP using PVDF sensors: ANN approach

Bar, H. N.; Bhat, M.R.; Murthy, C. Siva Ram

doi:10.1016/j.compstruct.2003.10.019

Cited by 47 publications

(20 citation statements)

References 14 publications

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“…A well trained ANN is a useful tool for systematic parametric studies and characterization of failure mechanisms of composites. It was used by several researchers due to reduction in time and cost of required experimental measurements [11][12][13][14]. ANN can be used to simulate the relationship between process parameters and performance of composite material by process optimization for its design and prediction of mechanical properties before fabrication/testing [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Kopparthi

Kundavarapu

Dasari³

et al. 2019

SN Appl. Sci.

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The present work is aimed at determining mechanical properties of chopped strand glass fiber reinforced composite laminates manufactured based on the design of experiments by resin transfer molding at various injection pressures with 4, 5 and 6 layers. Response surface methodology was implemented to the experimental data for evaluating the effect of number of layers and resin injection pressure on mechanical properties and void content. Teaching learning based optimization (TLBO) has been proposed to predict optimal (maximum) mechanical properties of composite by optimizing the number of layers and injection pressure. Artificial neural network (ANN) with feed forward back propagation algorithm was also used to predict the responses and compare with experimental and TLBO results. It was found that the predicted values of responses from TLBO and ANN are good in agreement with experimental results.Keywords Artificial neural network (ANN) · Teaching learning based optimization (TLBO) · Glass fiber reinforced plastic (GFRP) · Resin transfer molding (RTM) · Mechanical properties

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Section: Introductionmentioning

confidence: 99%

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Kopparthi

Kundavarapu

Dasari³

et al. 2019

SN Appl. Sci.

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“…Artificial Neural Network (ANN) [1] analogous to biological neural system is an adaptive computer program which provides solutions to problems like complex data collections. In this approach relationship between input and output parameters are developed through a training process in which sets of inputs are applied to the network and the resulting outputs are compared with the known results.…”

Section: Introductionmentioning

confidence: 99%

Defect Characterisation of GFRP Cross Ply Laminates using Artificial Neural Networks

Karthik¹,

Balaguru²,

Sivaraman³

et al. 2015

IJERT

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An experimental work has been carried out to characterise the defects of post impacted Glass/Epoxy composite laminates using online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from ten-layered glass fibre (200 MIL cloth) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression moulding machine for 24 hours. 25 test specimens (ASTM D3039 standard) were prepared from the cross-ply laminates using water jet cutting machine. 21 specimens were subjected to impact load from three different heights using CEAST Fractovis Drop Impact machine. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using 30 kN INSTRON 3367 universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. These AE parameters are then used to characterise the defects in composite materials using Fuzzy C-means clustering algorithm associated with Principal Component Analysis. Artificial Neural Network technique is used in the process of getting the results. The acquired results can be used for online health monitoring through which failure of composite components can be identified at the initial stages.

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“…Bar et al . [18] used surface‐mounted PVDF film to identify the failure modes of three different glass fibre‐reinforced composites during tensile load using an artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Impacted Aramid‐Reinforced Composites by Embedded PVDF Acoustic Emission Sensors

Caneva

Rosa

Sarasini

2008

Strain

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An embedded piezoelectric [poly(vinylidene fluoride) (PVDF)] thin film sensors system for acoustic emission (AE) was realized to investigate the possibility of monitoring, in real time, the post-impact damage in aramid woven fabric-reinforced epoxy. The same sensors have been used in a previous work on similar specimens tested in flexure but not previously impacted, with the aim of verifying the suitability of these sensors to be embedded and their ability to detect AE signals under loading. This work is a continuation of the previous one aiming at evaluating the ability of these embedded PVDF sensors to point out the presence of impact damage, issue widely studied in literature.Aramid fibre/epoxy composite specimens with embedded PVDFs, previously impacted at different energies, namely 5, 10 and 15 J, were tested using three-point bending tests. It appeared from mechanical tests that the flexural strength decreased passing from non-impacted specimens to those impacted with the highest energy and that the embedment of PVDFs in the laminates did not markedly affect the structural integrity of the impacted composites. The degree of impact damage, represented by the decrease in mechanical properties, has been correlated with the AE activity by means of a parametric analysis of the AE signals detected during post-impact mechanical tests.

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Identification of failure modes in GFRP using PVDF sensors: ANN approach

Cited by 47 publications

References 14 publications

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Defect Characterisation of GFRP Cross Ply Laminates using Artificial Neural Networks

Monitoring of Impacted Aramid‐Reinforced Composites by Embedded PVDF Acoustic Emission Sensors

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