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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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In order to optimize the tensile performance measurement of wool and other fiber materials, the present paper proposes a novel characterization method based on acoustic emission detection of fiber fracture acoustic signals, which can characterize the tensile properties of materials. When the fiber material is stretched and fractured, part of the elastic potential energy accumulated during the stretching process will propagate into the air in the form of oscillating sound waves, which will carry the tensile property information of the fiber material. Firstly, the signal is de-noised by wavelet transform and the waveform parameters are extracted. Secondly, the amplitudes in the characteristic frequency interval (As-CFI) of the spectrum are extracted by fast Fourier transform, and principal component analysis is utilized to reduce the parameter dimension. Finally, the waveform parameters and the spectral parameters are respectively regression analyzed with tensile parameters. The result shows that there is a clear linear correlation between the tensile parameters of wool fibers and the acoustic signal parameters. In the multiple linear regression analysis of waveform parameters and As-CFI versus fracture stress, the correlation coefficients are all above 0.90.

The method to obtain the breaking strength and elongation distribution of fiber through numerous single fiber tests is tedious and time-consuming. In the present work, a method based on acoustic emission (AE) signals generated by fiber fracture during the fiber bundle test has been developed for estimating the distribution of single wool breaking strength and elongation. AE detection is performed simultaneously during the fiber extension. According to the AE signal, it is proved that every individual fiber break can be detected, and the failure probability distribution of wool elongation can be obtained. Based on this, the single wool breaking strength is estimated from the tensile response of the fiber bundle, and then its distribution can be deduced. Finally, the distributions of breaking strength and elongation determined by the bundle test are compared with that obtained from the single fiber samples. The results show that the method developed in this work can be used to estimate the breaking strength and elongation of the single fiber within the bundle. Their cumulative probability distributions are similar to the results of single fiber sample tests, especially the distribution of the breaking strength.