A data-based modelling scheme for estimating residual stress from Barkhausen noise measurements

Sorsa, Aki; Leiviskä, Kauko; Santa-aho, Suvi; Lepistö, Toivo

doi:10.1784/insi.2012.54.5.278

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…The traditional features computed are the RMS value, peak width, and peak position. Peak height is not used here because it is reported to have a great correlation with the RMS value [5] and thus does not provide any additional information for the model. The sweep measurements were taken from five locations on each sample.…”

Section: Measurements and Data Pre-processingmentioning

confidence: 99%

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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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Section: Measurements and Data Pre-processingmentioning

confidence: 99%

“…Some studies were identified in the literature that modelled material properties and BN. In [2] and [5] for example, linear models were identified for mapping the interactions between BN, and residual stress and hardness. Use of nonlinear model structures can also be found in the literature.…”

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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“…Traditional MBN features such as root mean square (RMS) and MBN energy are often used for stress detection [9,10].…”

Section: Comparison With Traditional Mbn Featuresmentioning

confidence: 99%

A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

2019

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This paper reports on a new feature extraction method for detection of applied stress using magnetic Barkhausen noise (MBN). Some previous methods for extracting MBN features need to choose a suitable threshold so that these features can have good linearity and low dispersion, such as pulse count and full width at 25, 50 and 75% of the maximum amplitude. A new approach has been proposed for selecting the appropriate threshold for MBN features adaptively using a genetic algorithm (GA). The criterion for selecting the threshold is the lowest standard deviation of features and new proposed ‘overlap’ of features. In order to verify the effectiveness of the adaptive pulse count feature for stress detection, different modelling techniques are compared, including multivariable linear regression (MLR) and multilayer perceptron (MLP). The results obtained have proven that adaptive threshold features can effectively distinguish between different stress conditions compared with traditional MBN features.

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“…The Barkhausen effect specifically refers to the phenomenon in which the magnetic flux of a magnetized material changes discontinuously due to magnetic domain inversion during magnetization [ 1 ]. As an important nondestructive testing (NDT) method, the magnetic Barkhausen noise (MBN) signal is sensitive to the changes in many material properties and has many applications in the fields of material stress and hardness detection [ 2 , 3 , 4 , 5 ], metal fatigue state analysis [ 6 ], metal microstructure transformation, and grain size measurement [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Intensity changes in an MBN signal with material state transformation are usually reflected and described by the representative parameters calculated in time-frequency (TF) domain such as the amplitude, energy, root mean square (RMS), waveform full width at half maximum (FWHM), envelope, peak time, threshold, and power spectrum [ 3 , 4 , 5 , 8 , 9 , 10 ]. However, affected by the microscopic magnetic anisotropy of the material itself, measurement performance, and experimental magnetization parameters (such as magnetization intensity and frequency, excitation waveform), the MBN has an obvious stochastic nature and the application of more automatic signals processing procedures used for extraction, selection, and fusion of signal features containing critical and distinctive information about the material properties are urgently required.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Hou

Zheng

et al. 2020

Sensors

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The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback–Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

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A data-based modelling scheme for estimating residual stress from Barkhausen noise measurements

Cited by 8 publications

References 21 publications

Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement

Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement

A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

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