No 1 (2023)

It is shown that in order to increase the sensitivity of air-coupled ultrasonic through transmission testing of products made of polymer composite materials, it is necessary to develop highly sensitive low-frequency wideband air-coupled piezoelectric transducers. The methods of providing both high sensitivity and a wideband of air-coupled ultrasonic piezoelectric transducer are considered. Air-coupled ultrasonic highly sensitive broadband piezoelectric transducer based on the use of mosaic contact piezoelectric transducer technology, the choice of optimal matching layers and the use of various options for excitation of piezoelectric transmitter have been proposed and developed. It is shown that by use of mosaic air-coupled low-frequency broadband piezoelectric transducers, it is possible to ensure high sensitivity of testing and ensure the accuracy of measurement of acoustic characteristics (ultrasound velocity, density, etc.) with air-coupled ultrasonic low-frequency through transmission testing of large-sized objects made of polymer composite materials.

In this paper, the possibilities of using non-destructive acoustic control to determine the features of the spatial distribution of local plastic deformations during deformation of flat samples made of AMg61 alloy were investigated. The methodological features of using the acoustic anisotropy parameter to study the patterns of changes in spatial inhomogeneities of the field of local plastic deformations are analyzed. A computational and experimental method for determining the acoustic anisotropy parameter is proposed, which allows to correctly determine not only its magnitude, but also the directions of the local axes of acoustic anisotropy. The sources of errors of the proposed methodology, the limits of its applicability, as well as the requirements for hardware and software for its implementation are analyzed. The results of experimental studies carried out on samples of weakly anisotropic aluminum alloy AMg61 are compared with the representations of autowave mechanics of plasticity of metals. An engineering algorithm for determining the early localization of zones of loss of stability during plastic deformation of samples is proposed. The possibility of creating a methodology for assessing the plasticity resource of a material during its plastic shaping is shown.

Low-cycle fatigue tests in the elastic-plastic strain region of 09G2S steel specimens manufactured with a laser 3D printer by selective laser melting method (SLS steel) were carried out. The major hysteresis loops and field dependences of the reversible magnetic permeability were measured. It has been established that normalization at 980 °C (1 hour) reduces the ultimate strength of steel 09G2S in 2 times (502 MPa) and increases the relative elongation almost 6 times (34.6%), bringing this steel closer to cast steel 09G2S. The magnetic properties (Нс, Br, µmax) of cast and SLM normalized steel before and after cyclic tests are similar. The main changes in these properties of both cast and SLM steel are observed at the initial stage of low-cycle tests, a further increase in the number of cycles (up to the destruction of the tested samples) does not lead to their significant change. The nature of the change in the magnetoelastic field Hσ, determined from the experimental field dependences of the reversible magnetic permeability, during low-cycle tests for cast and SLM steels is radically different: for cast 09G2S steel the magnetoelastic field Hσ practically does not change with increasing number of cycles, whereas for SLM 09G2S steel a sharp increase of Hσ value by 30% is observed during the first test cycles, which is most likely associated with an increase in residual mechanical stresses.

In this paper, we choose convolution neural network (CNN) as the method to diagnosis weak fault of rolling bearings. In order to improve the training effect of CNN, different two-dimensional image conversion algorithms which include Gramian angular sum difference fields, wavelet time-frequency diagram, Markov transition field are introduced in to convert one-dimensional time series of bearing vibration signals into images. To relieve the pressure of hardware calculation and shorten the time of training and validation, we use the piecewise aggregate approximation (PAA) to compress the data as much as possible while preserving the whole signal information. We add the batch normalization layer to avoid the gradient saturation problem of ReLU function and minibatch method is used to overcome the instability of stochastic gradient descent with momentum (SGDM) while designing CNN. Each kind of images are made as the training sample, and the results show that both the wavelet time-frequency diagram and the Gramian sum or difference angle field diagram can better identify the fault state, and the wavelet time-frequency diagram was relatively better. By comparing with different recurrent neural network (RNN) diagnosis models, the validity of the model was proved. At the same time, the model is applied to the performance degradation identification of fault parts, and the results shows that the model can effectively identify the degradation of inner ring, outer ring and rolling body, while the accuracy of inner ring and the outer ring is better. This paper provides a new idea for weak fault diagnosis of rolling bearings.