Micromagnetic and Quantitative Prediction of Surface Hardness in Carbon Steels Based on a Joint Classification-Regression Method

Wang, Xianxian; He, Cunfu; Liu, Xiucheng; Xing, Zhixiang; Yan, Zhengxiang

doi:10.1007/s10921-022-00893-y

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…The whole system is composed of detection circuits encased in the main body of the instrument, a sensor, and a computer for supporting control software. The reliability of the used micro-magnetic testing system had been verified in our previous work [19,21].…”

Section: Experimental Systemmentioning

confidence: 79%

“…Sheng et al [17] and Mirzaee et al [18] employed the generalized regression neural network model and radialbasis function neural networks in model training and successfully realized nondestructive evaluation of mechanical properties of cold-rolled steel strips. We [17,[19][20][21] also proved that the micro-magnetic testing method combined with feedforward neural network (FF-NN) models outperformed the conventional ways in quantitatively predicting the surface hardness, case depth, and stress.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

Wang,

He,

et al. 2023

Meas. Sci. Technol.

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The correlation between multiple patterns of micromagnetic signatures and the mechanical properties (yield strength (Rp) and tensile strength (Rm) of high-strength steel (referred to as DP590 steel in Chinese standards) was investigated in this study. Feedforward neural network (FF-NN) models with carefully selected magnetic features as input nodes were established for quantitative prediction of yield strength and tensile strength. The accuracy FF-NN models highly relied on the quality of calibration specimens and the way of selecting magnetic features. The variations of the measured target properties were used to evaluate the quality of the calibration specimens. The specimens with similar yield strength (or tensile strength) were merged to share the same target properties in the model training process. The results demonstrated that merging proper target properties (label) could improve the performance of the models in quantitative prediction of yield strength and tensile strength in DP590 steels. In addition, the performances of FF-NN models combined with the algorithms of ReliefF and ReliefF + clustering were evaluated. The comparison results proved that the FF-NN models employing input nodes selection strategy of ReliefF + clustering realized the advantages of smaller dimensions of input nodes, less training time consumption at the cost of slight accuracy reduction.

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Section: Experimental Systemmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

Wang,

He,

et al. 2023

Meas. Sci. Technol.

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“…In micromagnetic detection methods, multiple magnetic signals (magnetic Barkhausen noise, tangential magnetic field (TMF), and magnetic incremental permeability (EIP)) are combined together to characterize the mechanical properties of materials [20,25]. In this paper, a sensor (Figure 1a) was developed to simultaneously detect two types of magnetic signals (TMF and MBN).…”

Section: Experimental Devicementioning

confidence: 99%

“…Abundant magnetic features have been extracted from MBN signals using various methods for the characterization of mechanical properties such as surface hardness. In the commonly used MBN processing method, the MBN butterfly curve [20,21] is first plotted, and the peak value, peak position, and other characteristic values are then extracted from the plotted curve to establish the correlation model for the quantitative evaluation of surface hardness. VASHISTA et al [22] introduced two eigenvalues of counts and events to describe MBN signals, but the features were greatly affected by the artificial selection of the threshold and signal interception time.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

Wang,

Cai,

Liu

et al. 2024

Sensors

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The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction.

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“…Recent studies proved that enriching the parametric space of magnetic features could improve the prediction accuracy of models [ 11 , 12 ]. Therefore, in the proposed multifunctional micromagnetic evaluation method, micromagnetic signals together with other magnetic signatures (such as tangential magnetic field, eddy current, and magnetic hysteresis curve, etc.)…”

Section: Introductionmentioning

confidence: 99%

Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument

Xing

Wang

Ning

et al. 2023

Sensors

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The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related to instruments were seldom explored. The robustness of the prediction model considering the repeatability of instruments was seldom discussed. In this work, a self-developed multifunctional micromagnetic instrument was employed to perform the repeatability test with Cr12MoV steel. The repeatability of the instrument in measuring multiple magnetic features under both static and dynamic conditions was evaluated. The magnetic features for establishing the prediction model were selected based on the consideration of both the repeatability of the instrument and the ability of magnetic features in surface hardness evaluation. To improve the robustness of the model in surface hardness prediction, a modelling strategy considering the repeatability of the instrument was proposed. Through removing partial magnetic features with higher mean impact values from input nodes, robust evaluation of surface hardness in Cr12MoV steel was realized with the multifunctional micromagnetic instrument.

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Micromagnetic and Quantitative Prediction of Surface Hardness in Carbon Steels Based on a Joint Classification-Regression Method

Cited by 3 publications

References 12 publications

Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument

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