Prediction of impact damage tolerance of drop impacted WGFRP composite by artificial neural network using acoustic emission parameters

Ramasamy, Parthiban; Sampathkumar, S.

doi:10.1016/j.compositesb.2013.12.028

Cited by 45 publications

(9 citation statements)

References 23 publications

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“…These diagrams were further utilized for designing purposes. Ramasamy et al [190] used a feed forward ANN to predict the behavior of GFRP composites when subjected to drop impact test. Data fed to the ANN model was experimentally obtained by conducting a drop impact test and acoustic emission technique, wherein Purlin adaptive learning function along with the gradient descent algorithm was used.…”

Section: Neural Networkmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Neural Networkmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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show abstract

“…More recently, ANN has been employed as a tool to model material behaviour, and has an ability to model engineering materials with complex nonlinear characteristics. [13][14][15][16] Further, the ANN methodology has a well-known physical basis 17 and in addition it is also able to perform prediction of complex thermal behaviour 18 and impact damage behaviour 19 of materials.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network technique to predict dynamic fracture of particulate composite

Kushvaha

Kumar

Madhushri

2020

Journal of Composite Materials

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In this paper, the artificial neural network technique using a multi-layer perceptron feed forward scheme was used to model and predict the mode-I fracture behaviour of particulate polymer composites when subjected to impact loading. A neural network consisting of three-layers was employed to develop the network. Artificial neural network was constructed using six input parameters such as shear wave speed ( CS), density ( D), elastic modulus ( Ed), longitudinal wave speed ( CL), volume fraction ( Vf) and time ( t). The influence of input parameters on the output stress intensity factor and crack-initiation fracture toughness were found to be in the order of t > CS > D > Ed > CL > Vf. The degree of accuracy of prediction was 92.7% for stress intensity factor. In this regard, artificial neural network can be used in the modelling and prediction of fracture behaviour of particulate polymer composites under impact loading.

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“…Yet, we must first shortly discuss the power and considerations of the AE measurements, as these diagnostic and prognostic algorithms rely on the recorded AE data. AE data can be considered quite powerful as it has been used to, among others, detect damage initiation, 18,19 distinguish different damage types, 20,21 and to predict residual strength 22,23 and remaining useful life. 24,25 Yet, a great challenge with implementing AE technique is that its measurements are influenced by a variety of factors, such as sensor type, placement, and coupling.…”

Section: Introductionmentioning

confidence: 99%

Fusion-based damage diagnostics for stiffened composite panels

Broer

Galanopoulos

Benedictus

et al. 2021

Structural Health Monitoring

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Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.

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Prediction of impact damage tolerance of drop impacted WGFRP composite by artificial neural network using acoustic emission parameters

Cited by 45 publications

References 23 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Artificial neural network technique to predict dynamic fracture of particulate composite

Fusion-based damage diagnostics for stiffened composite panels

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