Textile failure analysis and mechanical characterization using acoustic emission technique

Ríos-Soberanis, Carlos Rolando

doi:10.1016/b978-0-08-101937-5.00011-7

Handbook of Materials Failure Analysis 2020

DOI: 10.1016/b978-0-08-101937-5.00011-7

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Textile failure analysis and mechanical characterization using acoustic emission technique

Carlos Rolando Ríos-Soberanis

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2020

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Cited by 2 publications

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“…In order to predict the tensile behavior of a material during its service life, it is important to evaluate its mechanical response under different types of external stresses by studying the initiation and progression of damage along with the effects induced by external environmental factors and degradation. 1 Predicting the tensile properties of material has been the main purpose of many studies. [2][3][4] When a material is stressed, there is a natural opposition to deformation (resistance) once a certain point is reached; system discontinuity appears in the materials as microdamage.…”

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Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission

2020

Textile Research Journal

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The acoustic emission (AE) technique is widely used at the present time for almost any kind of material characterization. The main aim of the present study was to predict the tensile strength of wool by using artificial neural networks and multiple linear regression analysis based on AE detection. With this aim, a number of single wool fibers were stretched to fracture and the signals at break were recorded by the AE technique. The energy, amplitude, duration, number of hits, average rectified value and root mean square value were used as input parameters to predict the strength of the wool. A feed-forward neural network with a backpropagation (BP) algorithm was successfully trained and tested using the measured data. The same input parameters were used by multiple stepwise regression models for the estimation of wool strength. The coefficients of determination of the BP neural network and stepwise regression indicate that there is a strong correlation between the measured and predicted strength of wool with an acceptable error value. The comparative analysis of the two modeling techniques shows that the neural network performs better than the stepwise regression models. Meanwhile, the relative importance of the input parameters was determined by using rank analysis. The prediction models established in the present work can be applied to AE studies of fiber bundles or fiber-reinforced composite materials.

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Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission

2020

Textile Research Journal

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“…The AE technique exhibits great potential for the characterization of failure mechanisms in composite materials. 2…”

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“…The AE technique exhibits great potential for the characterization of failure mechanisms in composite materials. 2 Giordano et al 3 performed spectral analysis on a single-carbon-fiber composite to evaluate the frequency spectrum content of the signal. The acquisition of different events during different tensile tests and their comparison showed that a clear acoustic fingerprint could be characterized in terms of frequency content.…”

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Characterization and analysis of the tensile and acoustic emission parameter distributions of single wool fibers

2020

Textile Research Journal

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Acoustic emission (AE) parameters of damaged material have discrete characteristics that are universally known. Such characteristics restrict wider use of AE analysis in materials such as textiles. In the present work, the best-fitted distribution models of AE signal energy are analyzed: amplitude, duration, tensile breaking strength, elongation, and specific work of wool fibers. The parameters of Weibull, normal, and log-normal distribution models are obtained by regression analysis. The chi-square, Kolmogorov–Smirnov, and maximum likelihood criterions are used to discriminate against the above models. The results show that the Weibull distribution is the best-fitted model for amplitude and elongation of wool. The best-fitted model for energy, strength, and specific work is the log-normal distribution. The differences between the cumulative distributions of the AE and tensile parameters are compared. It can be seen that strength also has a high correlation coefficient and a similarly cumulative distribution with energy and amplitude. Compared to amplitude, the relationship between energy and strength is supposed to be stronger.

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Textile failure analysis and mechanical characterization using acoustic emission technique

Cited by 2 publications

References 27 publications

Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission

Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission

Characterization and analysis of the tensile and acoustic emission parameter distributions of single wool fibers

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