Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies
Abstract
:1. Introduction
2. Methods
2.1. Online Micro-Magnetic NDT
2.2. Experiments
2.2.1. Temperature Influence Experiment
2.2.2. Tension Influence Experiment
2.2.3. Lift-Off Influence Experiment
3. Influence Factors Analysis
3.1. Analysis of Experimental Results
3.2. Analysis of Pearson’s Correlation Coefficients
3.3. Electromagnetic Characteristics Regression Based on Lift-Off
4. Prediction Model and Results
4.1. Improved GRNN Model Based on GMC Algorithm
4.2. Model Evaluation Functions
4.3. Prediction Results
5. Conclusions
- Temperature and tension can subsequently affect the micro-magnetic characteristics by altering the domain structure and domain walls’ motion properties;
- The lift-off was determined as the largest influence factor among influence factors, which could be eliminated by quartic polynomial fitting;
- The number of training models were optimized by the improved GRNN model based on GMC, which could improve the detection accuracy;
- The proposed system, embedded in the production process, will neither damage nor affect the usability of the steel strip and accurately obtain the steel strip’s mechanical properties.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Symbol | Unit | Description | Testing Method |
A3, A5, A7 | A/cm | Amplitude of 3rd, 5th, and/or 7th harmonics of Ht | Tangential magnetic field strength(TMF) |
P3, P5, P7 | Rad | Phase of 3rd, 5th, and/or 7th harmonics of Ht | |
UHS | A/cm | Amplitude summation; A3 + A5 + A7 + A9 | |
K | % | Harmonic distortion | |
Hco | A/cm | Coercive Field Strength, derived from TMF | |
Hro | A/cm | Upper Harmonics of Ht at zero-crossing | |
Vmag | V | Amplitude output voltage | |
MMAX | V | Maximum of the M(H) curve within one cycle | Barkhausen noise(BN) |
MMEAN | V | Time period average of the M(H) curve within one cycle | |
MR | V | Measured value of M(H) at H = 0 A/cm | |
HCM | A/cm | Coercive Field Strength, derived from the M(H) curve; H = HCM for M = MMAX | |
DH25M DH50M DM75M | A/cm | Expansion of the M(H) curve at M = 0.25 MMAX, 0.50 MMAX, and 0.75 MMAX | |
UMAX | V | Maximum of the u(H) curve within one cycle | Incremental permeability(IP) |
UMEAN | V | Time period average of the U(H) curve within one cycle | |
UR | V | Measured value of U(H) at H = 0 A/cm | |
HCU | A/cm | Coercive Field Strength, derived from the U(H) curve; H = HCU for U = UMAX | |
DH25U DH50U DM75U | A/cm | Expansion of the U(H) curve at U = 0.25 UMAX, 0.50 UMAX, and 0.75 UMAX | |
Re1–Re4 | V | Real part of the EC signal at Frequency No. 1, 2, 3, and 4 | Multi-frequency Eddy Current (EC) |
Im1–Im4 | V | Imaginary part of the EC signal at Frequency No. 1, 2, 3, and 4 | |
Mag1–Mag4 | V | Value of the EC signal at Frequency No. 1, 2, 3, and 4 | |
Ph1–Ph4 | Rad | Phase of the EC signal at Frequency No. 1, 2, 3, and 4 |
References
- Wolter, B.; Gabi, Y.; Conrad, C. Nondestructive testing with 3MA—An overview of principles and applications. Appl. Sci. 2019, 9, 1068. [Google Scholar] [CrossRef] [Green Version]
- Batista, L.; Rabe, U.; Altpeter, I.; Hirsekorn, S.; Dobmann, G. On the mechanism of nondestructive evaluation of cementite content in steels using a combination of magnetic Barkhausen noise and magnetic force microscopy techniques. J. Magn. Magn. Mater. 2014, 354, 248–256. [Google Scholar] [CrossRef]
- Batista, L.; Rabe, U.; Hirsekorn, S. Magnetic micro-and nanostructures of unalloyed steels: Domain wall interactions with cementite precipitates observed by MFM. NDT E Int. 2013, 57, 58–68. [Google Scholar] [CrossRef]
- Vashista, M.; Moorthy, V. On the shape of the magnetic Barkhausen noise profile for better revelation of the effect of microstructures on the magnetisation process in ferritic steels. J. Magn. Magn. Mater. 2015, 393, 584–592. [Google Scholar] [CrossRef] [Green Version]
- Zhu, B.; Xu, Z.; Wang, K.; Zhang, Y. Nondestructive evaluation of hot stamping boron steel with martensite/bainite mixed microstructures based on magnetic Barkhausen noise detection. J. Magn. Magn. Mater. 2020, 503, 166598. [Google Scholar] [CrossRef]
- Krause, A.K.; Underhill, P.R.; Krause, T.W.; Clapham, L. Magnetic Flux Density Superposition in Nonlinear Anisotropic Ferromagnetic Material and Resulting Magnetic Barkhausen Noise. IEEE. T. Magn. 2021, 57, 1–7. [Google Scholar] [CrossRef]
- Altpeter, I.; Dobmann, G.; Kröning, M.; Rabung, M.; Szielasko, S. Micro-magnetic evaluation of micro residual stresses of the IInd and IIIrd order. NDT E Int. 2009, 42, 283–290. [Google Scholar] [CrossRef]
- Wang, H.; Dong, L.; Wang, H.; Ma, G.; Xu, B.; Zhao, Y. Effect of tensile stress on metal magnetic memory signals during on-line measurement in ferromagnetic steel. NDT E Int. 2021, 117, 102378. [Google Scholar] [CrossRef]
- Yelbay, H.I.; Cam, I.; Gür, C.H. Non-destructive determination of residual stress state in steel weldments by Magnetic Barkhausen Noise technique. NDT E Int. 2010, 43, 29–33. [Google Scholar] [CrossRef]
- Sánchez, J.C.; De Campos, M.F.; Padovese, L.R. Magnetic Barkhausen emission in lightly deformed AISI 1070 steel. J. Magn. Magn. Mater. 2012, 324, 11–14. [Google Scholar] [CrossRef]
- Perevertov, O.; Thielsch, J.; Schäfer, R. Effect of applied tensile stress on the hysteresis curve and magnetic domain structure of grain-oriented transverse Fe-3% Si steel. J. Magn. Magn. Mater. 2015, 385, 358–367. [Google Scholar] [CrossRef]
- Ding, S.; Tian, G.; Dobmann, G.; Wang, P. Analysis of domain wall dynamics based on skewness of magnetic Barkhausen noise for applied stress determination. J. Magn. Magn. Mater. 2017, 421, 225–229. [Google Scholar] [CrossRef] [Green Version]
- Qiu, F.; Jovičević-Klug, M.; Tian, G.; Wu, G.; McCord, J. Correlation of magnetic field and stress-induced magnetic domain reorientation with Barkhausen Noise. J. Magn. Magn. Mater. 2021, 523, 167588. [Google Scholar] [CrossRef]
- Uchimoto, T.; Takagi, T.; Konoplyuk, S.; Abe, T.; Huang, H.; Kurosawa, M. Eddy current evaluation of cast irons for material characterization. J. Magn. Magn. Mater. 2003, 258, 493–496. [Google Scholar] [CrossRef]
- Gupta, B.; Uchimoto, T.; Ducharne, B.; Sebald, G.; Miyazaki, T.; Takagi, T. Magnetic incremental permeability non-destructive evaluation of 12 Cr-Mo-WV Steel creep test samples with varied ageing levels and thermal treatments. NDT E Int. 2019, 104, 42–50. [Google Scholar] [CrossRef]
- Desvaux, S.; Duquennoy, M.; Gualandri, J.; Ourak, M. The evaluation of surface residual stress in aeronautic bearings using the Barkhausen noise effect. NDT E Int. 2004, 37, 9–17. [Google Scholar] [CrossRef]
- O’Sullivan, D.; Cotterell, M.; Tanner, D.; Mészáros, I. Characterisation of ferritic stainless steel by Barkhausen techniques. NDT E Int. 2004, 37, 489–496. [Google Scholar] [CrossRef]
- Wang, P.; Zhu, S.; Tian, G.Y.; Wang, H.; Wilson, J.; Wang, X. Stress measurement using magnetic Barkhausen noise and metal magnetic memory testing. Meas. Sci. Technol. 2010, 21, 055703. [Google Scholar] [CrossRef]
- Wang, P.; Zhu, L.; Zhu, Q.; Ji, X.; Wang, H.; Tian, G.; Yao, E. An application of back propagation neural network for the steel stress detection based on Barkhausen noise theory. NDT E Int. 2013, 55, 9–14. [Google Scholar] [CrossRef]
- Wang, P.; Gao, Y.; Yang, Y.; Tian, G.; Yao, E.; Wang, H. Experimental studies and new feature extractions of MBN for stress measurement on rail tracks. IEEE Trans. Magn. 2013, 49, 4858–4864. [Google Scholar] [CrossRef]
- Boller, C.; Altpeter, I.; Dobmann, G.; Rabung, M.; Schreiber, J.; Szielasko, K.; Tschuncky, R. Electromagnetism as a means for understanding materials mechanics phenomena in magnetic materials. Materialwiss. Werkst. 2011, 42, 269–278. [Google Scholar] [CrossRef]
- Grimberg, R.; Leitoiu, S.; Bradu, B.; Savin, A.; Andreescu, A. Magnetic sensor used for the determination of fatigue state in ferromagnetic steels. Sens. Actuat. A-Phys. 2000, 81, 371–373. [Google Scholar] [CrossRef]
- Ryu, K.; Nahm, S.; Park, J.; Yu, K.; Kim, Y.; Son, D. A new non-destructive method for estimating the remanent life of a turbine rotor steel by reversible magnetic permeability. J. Magn. Magn. Mater. 2002, 251, 196–201. [Google Scholar] [CrossRef]
- Li, K.; Li, L.; Wang, P.; Liu, J.; Shi, Y.; Zhen, Y.; Dong, S. A fast and non-destructive method to evaluate yield strength of cold-rolled steel via incremental permeability. J. Magn. Magn. Mater. 2020, 498, 166087. [Google Scholar] [CrossRef]
- Jurkovič, M.; Kalina, T.; Zgútová, K.; Neslušan, M.; Pitoňák, M. Analysis of magnetic anisotropy and non-homogeneity of S235 ship structure steel after plastic straining by the use of barkhausen noise. Materials 2020, 13, 4588. [Google Scholar] [CrossRef]
- Pitoňák, M.; Neslušan, M.; Minárik, P.; Čapek, J.; Zgútová, K.; Jurkovič, M.; Kalina, T. Investigation of Magnetic Anisotropy and Barkhausen Noise Asymmetry Resulting from Uniaxial Plastic Deformation of Steel S235. Appl. Sci. 2021, 11, 3600. [Google Scholar] [CrossRef]
- Wang, P.; Zhang, Y.; Yao, E.; Mi, Y.; Zheng, Y.; Tang, C. Method of measuring the mechanical properties of ferromagnetic materials based on magnetostrictive EMAT characteristic parameters. Measurement 2021, 168, 108187. [Google Scholar] [CrossRef]
- Szielasko, K.; Wolter, B.; Tschuncky, R.; Youssef, S. Micromagnetic materials characterization using machine learning. Tm-Tech. Mess. 2020, 87, 428–437. [Google Scholar] [CrossRef]
- Maciusowicz, M.; Psuj, G.; Kochmański, P. Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network. Materials 2022, 15, 118. [Google Scholar] [CrossRef] [PubMed]
- Xiucheng, L.; Ruihuan, Z.; Bin, W.; Cunfu, H. Quantitative prediction of surface hardness in 12CrMoV steel plate based on magnetic Barkhausen noise and tangential magnetic field measurements. J. Nondestruct. Eval. 2018, 37, 38. [Google Scholar] [CrossRef]
- Gabi, Y.; Wolter, B.; Gerbershagen, A.; Ewen, M.; Braun, P.; Martins, O. FEM simulations of incremental permeability signals of a multi-layer steel with consideration of the hysteretic behavior of each layer. IEEE Trans. Magn. 2014, 50, 1–4. [Google Scholar] [CrossRef]
- Dobmann, G. Physical basics and industrial applications of 3MA–micromagnetic multiparameter microstructure and stress analysis. In Proceedings of the 10th European Conference on Nondestructive Testing, ECNDT 2010, Moscow, Russia, 7–11 June 2010. [Google Scholar]
- Dobmann, G.; Kern, R.; Altpeter, I.; Theiner, W. Quantitative hardening-depth-measurements up to 4 mm by means of micro-magnetic microstructure multiparameter analysis (3MA). NDT E Int. 1991, 24, 44. [Google Scholar] [CrossRef] [Green Version]
- Chiba, T.; Shimamura, D.; Tanigawa, K.; Fukami, H.; Suzuki, S.; Ohshima, T.; Ishiwata, N.; Nakatani, Y. Current-induced magnetic domain wall motion in Co/Ni nanowire at low temperature. Appl. Phys. Express 2011, 4, 063003. [Google Scholar]
- Fan, L.; Hu, J.; Su, Y.; Zhu, J. Influence of temperature on current-induced domain wall motion and its Walker breakdown. J. Magn. Magn. Mater. 2016, 401, 484–487. [Google Scholar] [CrossRef]
- Martinez, E.; Lopez-Diaz, L.; Alejos, O.; Torres, L. Thermally activated domain wall depinning in thin strips with high perpendicular magnetocrystalline anisotropy. J. Appl. Phys. 2009, 106, 043914. [Google Scholar] [CrossRef]
- Wang, Y.; Meydan, T.; Melikhov, Y. Quantitative evaluation of the effect of temperature on magnetic Barkhausen noise. Sensors 2021, 21, 898. [Google Scholar] [CrossRef]
- Liu, J.; Tian, G.; Gao, B.; Zeng, K.; Liu, Q.; Zheng, Y. Magnetic Barkhausen Noise Transient Analysis for Microstructure Evolution Characterization with Tensile Stress in Elastic and Plastic Status. Sensors 2021, 21, 8310. [Google Scholar] [CrossRef]
- Hu, R.; Wen, S.; Zeng, Z.; Huang, T. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 2017, 221, 24–31. [Google Scholar] [CrossRef]
- Specht, D.F. A general regression neural network. IEEE Trans. Neural. Networ. 1991, 2, 568–576. [Google Scholar] [CrossRef] [Green Version]
- Kageyama, S.; Mori, N.; Mugikura, S.; Tokunaga, H.; Takase, K. Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: A novel approach to evaluate uterine endometrioid carcinoma grade. Eur. Radiol. 2021, 31, 55–64. [Google Scholar] [CrossRef]
- He, Z.; Ho, C.-H. An improved clustering algorithm based on finite Gaussian mixture model. Multimed. Tools. Appl. 2019, 78, 24285–24299. [Google Scholar] [CrossRef]
- Göhring, A.; Mauder, M.; Kröger, P.; Mayr, C.; von Carnap-Bornheim, C.; Hilberg, V.; Grupe, G. Evidence for sea spray effect on oxygen stable isotopes in bone phosphate—Approximation and correction using Gaussian mixture model clustering. Sci. Total. Environ. 2019, 673, 668–684. [Google Scholar] [CrossRef]
- Andrews, J.L. Addressing overfitting and underfitting in Gaussian model-based clustering. Comput. Stat. Data Anal. 2018, 127, 160–171. [Google Scholar] [CrossRef]
- Eidelman, A. Python Data Science Handbook by Jake VANDERPLAS (2016). Stat. Soc. 2020, 8, 45–47. [Google Scholar]
Strip Number | Tension (kN) | Lift-Off (mm) | Temperature (℃) | Micro-Electromagnetic Characteristics | Rm (MPa) | Rp0.2 (MPa) | A (%) |
---|---|---|---|---|---|---|---|
383 | 20 | 4.5 | 30 | …… | 500 | 400 | 30 |
Parameters | Value |
---|---|
Tension (kN) | 20–40 |
Lift-off (mm) | 4.5–5.5 |
Data Sampling Frequency of Micro-Magnetic Testing Device (Hz) | 4 |
Temperature (℃) | 20–30 |
Mechanical Properties | Evaluation Function | |
---|---|---|
RMSE (MPa) | ||
Rp0.2 | 71 | 22.14 |
Rm | 94 | 15.53 |
A | 87 | 2.73 |
Mechanical Properties | Evaluation Function | |
---|---|---|
RMSE (MPa) | ||
Rp0.2 | 95 | 8.58 |
Rm | 1 | 5.98 |
A | 96 | 1.76 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sheng, H.; Wang, P.; Tang, C. Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies. Materials 2022, 15, 2151. https://doi.org/10.3390/ma15062151
Sheng H, Wang P, Tang C. Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies. Materials. 2022; 15(6):2151. https://doi.org/10.3390/ma15062151
Chicago/Turabian StyleSheng, Hongwei, Ping Wang, and Chenglong Tang. 2022. "Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies" Materials 15, no. 6: 2151. https://doi.org/10.3390/ma15062151