A study of Barkhausen avalanche statistics through the critical disorder in a ferromagnetic thin film: Experimental investigation and theoretical modeling

Roy, Arnab; Kumar, Prajwal

doi:10.1016/j.jmmm.2019.165710

Cited by 4 publications

(1 citation statement)

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“…The extraction and analysis of micro-magnetic features that characterize the reversible and irreversible motion of magnetic domains, based on multi-source micro-magnetic NDT, can improve the accuracy of assessing the microstructure and stress state of ferromagnetic materials. The MBN signal, generated by the irreversible motion of the magnetic domain walls, could be used to describe the microstructure and the stress state of the ferromagnetic material [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Correspondingly, the MIP signal, generated by the reversible motion of the domain walls, could be also used to describe the microstructure and the stress state of the ferromagnetic material [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Stress and Microstructures Characterization Based on Magnetic Incremental Permeability and Magnetic Barkhausen Noise Techniques

Sheng,

Wang,

Yang

et al. 2024

Materials

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Both microstructure and stress affect the structure and kinematic properties of magnetic domains. In fact, microstructural and stress variations often coexist. However, the coupling of microstructure and stress on magnetic domains is seldom considered in the evaluation of microstructural characteristics. In this investigation, Magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) techniques are used to study the coupling effect of characteristic microstructure and stress on the reversible and irreversible motions of magnetic domains, and the quantitative relationship between microstructure and magnetic domain characteristics is established. Considering the coupling effect of microstructure and stress on magnetic domains, a patterned characterization method of microstructure and stress is innovatively proposed. Pattern recognition based on the Multi-layer Perceptron (MLP) model is realized for microstructure and stress with an accuracy rate higher than 97%. The results show that the pattern recognition accuracy of magnetic domain features and micro-magnetic features simultaneously as input parameters is higher than that of micro-magnetic features alone as input parameters.

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