Potential of magnetic Barkhausen noise analysis for in-process monitoring of surface layer properties of steel components in grinding

Jedamski, Rahel; Heinzel, Jonas; Rößler, Maximilian; Épp, Jérémy; Eckebrecht, J.; Gentzen, Jens; Pütz, Matthias; Karpuschewski, Bernhard

doi:10.1515/teme-2020-0048

Cited by 17 publications

(3 citation statements)

References 12 publications

(14 reference statements)

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“…A nondestructive technique that has shown significant promise in evaluating microstructural changes in steels is the Magnetic Barkhausen Noise (MBN) method [4]. MBN is based on detecting electrical noise generated by the movement of magnetic domain walls known as Weiss domains [5] in a ferromagnetic material when subjected to an external alternating magnetic field.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the magnetic Barkhausen noise technique for microstructural characterization of rail steel

Carvalho,

Sanchez,

Bustamante

et al. 2024

Preprint

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In the present work the feasibility of using Magnetic Barkhausen Noise (MBN) to identify variations in the microstructure of a commercial rail steel has been studied. To achieve this purpose a brand new rail, reference R260 was sectioned to obtain two samples that were subjected to quenching and normalizing heat treatments. The hardness and microstructure of the specimens were evaluated by conventional destructive and nondestructive evaluation. The MBN technique's sensibility to characterize different microstructures was studied, and the results were contrasted with hardness and residual stress measurements. The envelope of MBN signals proved to be useful to detect the presence of martensite at the surface of rail sections, mainly because of the high density of dislocations that is typical of this micro constituent in comparison with pearlite of ferrite. The MBN signals showed strong correlation with the changes in hardness and microstructure of the samples, being the normalized sample the one with the highest amplitude signal of MBN. In contrast, the quenched sample with martensite microstructure had a lower MBN intensity. The results of this work show the potential of MBN for nondestructive evaluation (NDE) of rails in the field, which could improve the capacity of early detection of defects in railway systems.

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Section: Introductionmentioning

confidence: 99%

Implementation of the magnetic Barkhausen noise technique for microstructural characterization of rail steel

Carvalho,

Sanchez,

Bustamante

et al. 2024

Preprint

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show abstract

“…For example, Heinzel et al [9] combined thermal modelling and experimental grinding studies to determine the critical residual stress state at the surface via BN measurements. Some very recent publications have utilised BN-based systems for in-process monitoring purposes inside the grinding machine [10,11]. Such an approach allows modification of the grinding parameters during processing and thus would produce more consistent surface quality.…”

Section: Introductionmentioning

confidence: 99%

Sub-Surface Analysis of Grinding Burns with Barkhausen Noise Measurements

et al. 2022

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Barkhausen noise (BN) measurements are commonly used for surface characterisation. However, often there is also a need to verify the sub-surface region because detrimental tensile stresses may be present after different manufacturing steps. Especially in a grinding burn, the surface stress may be compressive, but it changes quickly into tensile stress below the surface. The aim of this study was to find out whether regular surface-sensitive BN measurement is also sensitive to the stresses below the surface caused by grinding burns. More specifically, the aim was to study the relationship between BN features and sub-surface stresses and to identify a model that estimates sub-surface stresses. Real samples were collected from an actual process. The samples were cylindrical samples manufactured from commercial alloyed AISI/SAE L6 steel that was through-hardened prior to grinding. Barkhausen noise measurements were carried out for 42 grinding burn locations followed by X-ray diffraction-based residual stress surface measurements and residual stress depth profiles. Depth information was obtained through step-by-step electrolytic removal of thin layers. The stress profiles were pre-processed through interpolation and averaged stress was calculated as a function of depth below the surface. Correlation analysis was carried out to evaluate the relationships between BN features and stress at different depths and among BN features. The main outcome of the analysis was that BN measurement is dominated by the sub-surface tensile stresses rather than the compressive stress at the surface. It was also noticed that BN features form two groups, corresponding to average Barkhausen activity and magnetising field strength leading to maximum Barkhausen activity. Models for stress at different steps were identified systematically. The performance of the models for sub-surface stresses was reasonable with R2 values of around 0.85 and root mean squared error (RMSE) values of around 95 MPa. Based on the results, it is concluded that BN measurement provides information about sub-surface stresses and that stress can be evaluated through straightforward modelling, allowing fast detection of grinding burns.

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“…Further, these surface properties influence the grindability of the components as well as the micromagnetic detectability of grinding damages [1,2]. Therefore, the knowledge of these properties can allow determining optimal grinding parameters as a function of the component's initial state and can enable a reliable in-process micromagnetic monitoring of the grinding processes [3]. Besides the known destructive testing methods which are precise but time-consuming, the heat treatment condition can also be assessed non-destructively using micromagnetic methods such as Barkhausen noise analysis or the 3MA-technique [4].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Jedamski

Épp

2020

Metals

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Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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Potential of magnetic Barkhausen noise analysis for in-process monitoring of surface layer properties of steel components in grinding

Cited by 17 publications

References 12 publications

Implementation of the magnetic Barkhausen noise technique for microstructural characterization of rail steel

Implementation of the magnetic Barkhausen noise technique for microstructural characterization of rail steel

Sub-Surface Analysis of Grinding Burns with Barkhausen Noise Measurements

Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

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