An application of back propagation neural network for the steel stress detection based on Barkhausen noise theory

Wang, Ping; Zhu, Lei; Zhu, Qiujun; Ji, Xiaoli; Wang, Haitao; Tian, Guohui; Yao, Erdong

doi:10.1016/j.ndteint.2013.01.007

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Artificial Neural Network1

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Cited by 59 publications

(18 citation statements)

References 9 publications

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“…Thus, any change in grains configuration, due to the presence of stresses [15][16][17][18][19] or of lattice distortions, results in rearrangement of the magnetic domains' configuration.…”

Section: Introductionmentioning

confidence: 99%

An accurate evaluation of the residual stress of welded electrical steels with magnetic Barkhausen noise

Vourna

Ktena²,

Tsakiridis

et al. 2015

Measurement

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“…Thus, any change in grains configuration, due to the presence of stresses [15][16][17][18][19] or of lattice distortions, results in rearrangement of the magnetic domains' configuration.…”

Section: Introductionmentioning

confidence: 99%

An accurate evaluation of the residual stress of welded electrical steels with magnetic Barkhausen noise

Vourna

Ktena²,

Tsakiridis

et al. 2015

Measurement

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“…Back-propagation neural network (BPNN) is the most frequently-used ANN models in chemical research [14][15][16]. More than 80% ANN-chemical research used BPNN models to development their prediction and pattern recognition models [7].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

How to Address the Problems of Data Mining in Chemistry: The Application of Artificial Neural Networks

Liu

2015

IJDTA

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“…Nonlinear black-box modelling techniques have also been used. Backpropagation neural networks were used in [8] to predict the stress applied to A3 type steel. In [9], an adaptive neuro-fuzzy system (ANFIS) was used to evaluate the microstructure of dual-phase steel.…”

Section: Introductionmentioning

confidence: 99%

Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement

Sorsa

Santa-aho

Aylott

et al. 2019

Metals

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Nitriding is a heat treatment process that is commonly used to enhance the surface properties of ferrous components. Traditional quality control uses sacrificial pieces that are destructively evaluated. However, efficient production requires quality control where the case depths produced are non-destructively evaluated. In this study, four different low alloy steel materials were studied. Nitriding times for the samples were varied to produce varying case depths. Traditional Barkhausen noise and Barkhausen noise sweep measurements were carried out for non-destructive case depth evaluation. A prediction model between traditional Barkhausen noise measurements and diffusion layer hardness was identified. The diffusion layer hardness was predicted and sweep measurement data was used to predict case depths. Modelling was carried out for non-ground and ground samples with good results.

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An application of back propagation neural network for the steel stress detection based on Barkhausen noise theory

Cited by 59 publications

References 9 publications

An accurate evaluation of the residual stress of welded electrical steels with magnetic Barkhausen noise

An accurate evaluation of the residual stress of welded electrical steels with magnetic Barkhausen noise

How to Address the Problems of Data Mining in Chemistry: The Application of Artificial Neural Networks

Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement

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