scholarly journals Time-Response-Histogram-Based Feature of Magnetic Barkhausen Noise for Material Characterization Considering Influences of Grain and Grain Boundary under In Situ Tensile Test

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2350
Author(s):  
Jia Liu ◽  
Guiyun Tian ◽  
Bin Gao ◽  
Kun Zeng ◽  
Yongbing Xu ◽  
...  
Keyword(s):  
Grain Boundary ◽  
Tensile Stress ◽  
Material Properties ◽  
Ferromagnetic Material ◽  
Silicon Steel ◽  
Time Response ◽  
Barkhausen Noise ◽  
Time Interval ◽  
Magnetic Barkhausen Noise

Stress is the crucial factor of ferromagnetic material failure origin. However, the nondestructive test methods to analyze the ferromagnetic material properties’ inhomogeneity on the microscopic scale with stress have not been obtained so far. In this study, magnetic Barkhausen noise (MBN) signals on different silicon steel sheet locations under in situ tensile tests were detected by a high-spatial-resolution magnetic probe. The domain-wall (DW) motion, grain, and grain boundary were detected using a magneto-optical Kerr (MOKE) image. The time characteristic of DW motion and MBN signals on different locations was varied during elastic deformation. Therefore, a time-response histogram is proposed in this work to show different DW motions inside the grain and around the grain boundary under low tensile stress. In order to separate the variation of magnetic properties affected by the grain and grain boundary under low tensile stress corresponding to MBN excitation, time-division was carried out to extract the root-mean-square (RMS), mean, and peak in the optimized time interval. The time-response histogram of MBN evaluated the silicon steel sheet’s inhomogeneous material properties, and provided a theoretical and experimental reference for ferromagnetic material properties under stress.

Schadenserfassung auf Radsatzlaufflächen/Defect detection on wheelset treads – Measurement of Magnetic Barkhausen Noise to detect varying workpiece conditions

2019 ◽  
Vol 109 (11-12) ◽  
pp. 811-815
Author(s):  
B. Denkena ◽  
B. Bergmann ◽  
H. Blech
Keyword(s):  
Acoustic Emission ◽  
Process Parameters ◽  
Defect Detection ◽  
Material Properties ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Optimal Process ◽  
Process Reliability

Unterschiedliche Belastungshistorien von Eisenbahnrädern führen zu Werkstoffveränderungen in der Lauffläche. Diese verursachen sporadisches Werkzeugversagen und verringern so die Prozesssicherheit. Die Messung der Material- und Prozesseigenschaften mit Barkhausenrauschen und Körperschall erlauben, individuelle Bearbeitungsparameter für jedes Exemplar festzulegen. Gezeigt werden die Herausforderungen in der Radsatzbearbeitung, und welche Informationen sich durch die Messtechniken gewinnen lassen.   Different load histories of train wheels lead to high variance of material properties on the running tread. Those cause unpredictable tool break and reduce process reliability. The measurement of magnetic Barkhausen noise and acoustic emission allow to gain information of the workpiece and the running process, to find optimal process parameters for the reconditioning of every individual wheel. Typical issues in train wheel machining and results of measurements are presented.

Stress Measurement in Carbon Steel by Magnetic Barkhausen Noise Technique

2017 ◽  
Vol 751 ◽  
pp. 213-218
Author(s):  
Mai Noipitak
Keyword(s):  
Heat Treatment ◽  
Residual Stress ◽  
Carbon Steel ◽  
Tensile Stress ◽  
Treatment Process ◽  
Stress Measurement ◽  
Barkhausen Noise ◽  
Heat Treatment Process ◽  
Magnetic Barkhausen Noise ◽  
The Relationship

The Magnetic Barkhausen Noise (MBN) technique can evaluate the residual stresses in carbon steel and provide information about the relationship between residual stress level and MBN signal. This research work is based on the analysis of MBN signals obtained from carbon steel samples. ASTM A36 and A516 carbon steel were used to vary the residual stress by heat treatment process with 5 conditions: annealing, normalizing, quenching in oil, quenching in water and quenching in salt water. The microstructure and hardness of samples also were varied by these heat treatment processes. Twelve samples (including base materials) were cut to analyze the microstructure and hardness by the microscope and hardness testing machine. Reference materials from each condition were established to represent the MBN signals. The MBN technique was used to evaluate the residual stresses from heat treatment process on each reference material. Then each sample was prepared to tensile specimen. All specimens were applied static tension load below yield point. The load was increased at 25 N/mm2 (MPa) in increment. Each tensile stress level was measurement by MBN technique at 0 and 90 degree of direction of tension axis. The experimental results found that the MBN signal amplitude changed as the condition of heat treatment changed and the relationship between tensile stress and MBN signal showed linear correlation. This research is useful to understand and guide for establishing the reference materials for residual stress measurement by MBN technique.

Influence of tensile stress in steel cables on magnetic Barkhausen noise

1993 ◽  
Vol 29 (6) ◽  
pp. 2992-2994 ◽  
Author(s):  
J. Kivimaa ◽  
M. Moilanen ◽  
R. Rautioaho ◽  
H. Zhang
Keyword(s):  
Tensile Stress ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Steel Cables

scholarly journals A time series classification approach to non-destructive hardness testing using magnetic Barkhausen noise emission

2021 ◽  
Author(s):  
Martin Unterberg ◽  
Joachim Stanke ◽  
Daniel Trauth ◽  
Thomas Bergs
Keyword(s):  
Time Series ◽  
Product Quality ◽  
Sheet Metal ◽  
Barkhausen Noise ◽  
Manufacturing Processes ◽  
Time Series Classification ◽  
Magnetic Barkhausen Noise ◽  
Noise Emission ◽  
Material Parameters

AbstractThe process setup of manufacturing processes is generally knowledge-based and carried out once for a material batch. Industry experts observe fluctuations in product quality and tool life, albeit the process setup remains unchanged. These fluctuations are mainly attributed to fluctuations in material parameters. An in-situ detection of changes in material parameters would enable manufacturers to adapt process parameters like forces or lubrication before turbulences like unexpectedly high tool wear or degradation in product quality occurs. This contribution shows the applicability of a deep learning time series classification architecture that does not rely on handcrafted feature engineering for the classification of hardness fluctuations in a sheet-metal coil using magnetic Barkhausen noise emission. This methodology is not limited to the detection of hardness fluctuations in sheet-metal coils and can potentially be applied for the in-situ material property classification in different manufacturing processes and for different material parameters.

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