CLASSIFICATION AND IDENTIFICATION OF ACOUSTIC EMISSION SIGNALS UNDER DEFORMATION AND FRACTURE OF MATERIALS AT LOW TEMPERATURES ON THE BASIS OF THE APPROACHES OF ARTIFICIAL INTELLIGENCE AND NONLINEAR DYNAMICS

2018 ◽  
pp. 32-38
Author(s):  
Yu. G. Kabaldin ◽  
D. A. Shatagin ◽  
M. S. Anosov ◽  
M. V. Zhelonkin ◽  
A. A. Golovin
2021 ◽  
Vol 11 (15) ◽  
pp. 6718
Author(s):  
Aleksander Sendrowicz ◽  
Aleksander Omholt Myhre ◽  
Seweryn Witold Wierdak ◽  
Alexei Vinogradov

A current trend in mechanical testing technologies is to equip researchers and industrial practitioners with the facilities for non-destructive characterisation of the deformation and fracture processes occurring on different scales. The synergistic effect of such a combination of destructive and non-destructive techniques both widens and deepens existing knowledge in the field of plasticity and fracture of materials and provides the feedback sought to develop new non-destructive testing approaches and in situ monitoring techniques with enhanced reliability, accuracy and a wider scope of applications. The macroscopic standardised mechanical testing is still dominant in the research laboratories and industrial sector worldwide. The present paper reviews multiple challenges commonly faced by experimentalists, aiming at enhancing the capability of conventional mechanical testing by a combination of contemporary infrared thermography (IRT), rapid video imaging (RVI) with non-contact strain mapping possibilities enabled by the digital image correlation (DIC) method, and the acoustic emission (AE) technique providing unbeatable temporal resolution of the stochastic defect dynamics under load. Practical recommendations to address these challenges are outlined. A versatile experimental setup uniting the unique competencies of all named techniques is described alone with the fascinating possibilities it offers for the comprehensive characterisation of damage accumulation during plastic deformation and fracture of materials. The developed toolbox comprising practical hardware and software solutions brings together measuring technologies, data, and processing in a single place. The proposed methodology focuses on the characterisation of the thermodynamics, kinematics and dynamics of the deformation and fracture processes occurring on different spatial and temporal scales. The capacity of the proposed combination is illustrated using preliminary results on the tensile and fatigue behaviour of the fcc Inconel-625 alloy used as a representative example. Dissipative processes occurring in this alloy are assessed through the complex interplay between the released heat, acoustic emission waves, and expended and stored elastic energy.


2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]


2021 ◽  
pp. 11-14
Author(s):  

An intelligent system for predicting the fatigue strength of metals in a wide temperature range is developed using a specially trained neural network. The system makes it possible to predict the number of load cycles of a part to failure, as well as the start of formation and growth rate of fatigue cracks for different test conditions, including at low temperatures. Keywords: neural network, prediction of loading cycles, low temperatures, fatigue strength. [email protected]


2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Surojit Poddar ◽  
N. Tandon

Abstract This present article evaluates the state of starvation in a journal bearing using acoustic emission (AE) and vibration measurement techniques. A journal bearing requires a constant supply of oil in an adequate amount to develop a hydrodynamic film, thick enough to separate the surfaces and avoid asperity contacts. On a microscopic level, the surface interaction under starved lubrication results in deformation and fracture of asperities. This causes a proportionate increase in AE and vibration. The AE activities resulting from asperities interaction have significant energy in the frequency range of 100–400 kHz with peak frequencies in the range of 224–283 kHz. Further, the peak frequency shifts from the higher to lower side as the asperity interaction transits from the elastic to plastic contact. This information derived from the spectral analysis of AE signals can be used to develop condition monitoring parameters to proactively control the lubrication and prevent bearing failure.


2012 ◽  
Vol 170-173 ◽  
pp. 179-182
Author(s):  
Zhi Tao Ma ◽  
Yong Ping Wang ◽  
Sai Jiang Liang ◽  
Dong Chuan Gao

Rock acoustic emission a physical phenomenon during the rock deformation, it is also an effective method used to study the properties of rock damage. In this article, from the aspects of elastic energy, a discrete nonlinear dynamics analysis method was established based on physical cellular automata. Using this new method, the properties of acoustic emission during the rock deformation and damage were studied, and the results were compared with related previous research achievements, and the results show that this new method based on cellular automata is reasonable and effective.


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