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The literature review revealed that there is a need in industry to provide safe, economical and reliable methods for characterizing surface treatments, in particular, such as case hardening and hardening of steel components and structures, both at the production stage and during operation. Magnetic Barkhausen emission (MBE) has been successfully used to solve these problems, but has serious limitations in terms of measurement depth, and surface treatment methods such as laser hardening and case hardening often fall outside its scope. The limiting factor for MBE is the attenuation of the eddy currents signal during its propagation through the material, which leads to a maximum measurement depth of about 1 mm, depending on the excitation frequency, the analysis frequency range and the material properties. The use of magnetoelastic acoustic emission (MAE), which propagates through the entire test sample without eddy currents interference, is promising, thus the measurement depth for MBE is much greater than for MBE. The possibility of using the MAE signal for characterizing surface treatment is substantiated. A heuristic model for estimating the thickness of the modified layer of structural elements made of ferromagnetic material is developed and a corresponding methodology is proposed. The essence of the method is as follows: a study is carried out using the MAE method for different frequencies of magnetization reversal of the original sample and the sample with a modified surface layer, the relative difference in the sums of the amplitudes of the recorded MAE signals is found, the frequency starting from which this difference will be constant also for this frequency, using the formula for the penetration depth of an alternating magnetic field or taking into account the dimensions of the sample is established, the thickness of the modified surface layer by numerical methods is estimated. The developed method was verified on samples of 17G1S steel in the delivery state and with an artificially plastically deformed upper layer, and the effective thickness of this upper layer of the sample is estimated.

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