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On the example of aluminum alloy AMg5 м plastic deformation and fracture under static loading was investigated. For the material being loaded, a stage of linear hardening, two stages of parabolic hardening and a fracture stage were identified. A change in the form of acoustic emission signals was established with a change in the region of strain hardening. The plastic yielding in the first region was accompanied by the formation of an acoustic emission peak, which was replaced in the next stage by high-amplitude oscillations. Further, for the region of discontinuous yielding, bursts of signals of different amplitude were observed, reflecting the dynamics of the formation of localized deformation bands. The change in the acoustic emission signals reflected the evolution of the deformation hardening processes. To describe the effect of the stagedness of deformation processes on the parameters of acoustic emission, the initial acoustic-emission signal was divided into separate time blocks. To each of these blocks, a discrete wavelet transform was applied. It characterized the time dependence of the waveform on the specific section of the strain hardening curve corresponding for each block. The obtained wavelet decomposition coefficients were processed using principal component analysis. They were plotted on the plane of the first principal components. The points of the multidimensional space corresponded to different regions were divided into partially overlapping clusters. The results of the work showed that the wavelet decomposition coefficients of acoustic emission signal could be used for diagnostics of the stages of strain hardening in aluminum alloys.

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The acoustic emission (AE) method is widely used in investigations of the reconstruction dynamics of material structures under the action of an external force. The signal recorded by AE equipment depends in a complicated way on the processes taking place inside the material and is the response from a sample-detector resonance system. This leads to the distortion of the input characteristics of AE sources and creates problems with the quantitative criteria of the diagnostics. A large number of this method's information parameters must therefore be used in practice [1,2].The method for reconstructing the statistical characteristics of AE sources [2] requires the use of AE equipment that allows a multiparameter analysis of the recorded signal. The use of computer-aided measurement facilities substantially improves the trustworthiness of the results obtained. To obtain the greatest possible amount of information on a variable signal, it is necessary to obtain a set of samples of it at the sampling frequency, which should be at least twice as high as the maximum frequency of the signal spectrum. Similar devices are widely used for the automating of physics experiments [3].In some cases, it is possible to synthesize the input signal over a smaller volume of fixed data. In this work, we consider a method for recording AE signals that was developed for resonance AE detectors and is intended for the comprehensive analysis of AE information parameters.In the AE studies, the AE energy E , total AE signal count N , count rate , and a number of the other characteristics considered in [1] are used as informative parameters. In order to measure E , it is necessary to use root-mean-square voltmeters, which as a rule have a small dynamic range, and hence reliably operate only in the presence of continuous AEs. The oscillation method, in which the number of AE pulsations exceeding a preset discrimination threshold [2] is counted, is Ṅ used to measure N and . This method works reliably for both discrete and continuous AEs but does not allow one to determine the energy characteristics of the process.To extend the oscillation method, we suggest that the maximum amplitude of each pulsation and its recording time be measured, along with the shape of the electrical pulse. This can be done most easily for resonance detectors that have increased sensitivity; these ensure the greatest possible signal-to-noise ratios and are therefore widely used in practice for studying AEs. The output signals of such detectors are in the form of wave packets (flashes) with an exponentially decaying oscillation amplitude at cyclic filling frequency ω ; this amplitude depends on the resonance behavior of the AE detector. As is shown in Fig. 1, each pulsation in this packet is characterized by ordinal number i , peak voltage U mi , and recording time t ( i ), and can be approximated by part of the sinusoid:where τ = tt ( i ). With a data array containing the amplitudes of each pulsation and the times of their arrival, it is possible to obtain the main parame...