Non-destructive determination of microstructural/mechanical properties and thickness variations in API X65 steel using magnetic hysteresis loop and artificial neural networks

Mirzaee, Ali; Kahrobaee, Saeed; Akhlaghi, Iman Ahadi

doi:10.1080/10589759.2019.1662901

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…The sources of random errors include the stochastic process of domain motion, slight fluctuation in electrical parameters of instruments, the minor difference in the contact state between sensors and specimen surface, etc. Improving the prediction accuracy of intelligent models through structural optimization of neural networks has been extensively explored [ 15 , 16 ], but the robustness of the prediction model considering the repeatability of the multifunctional micromagnetic instrument was seldom investigated. If the robustness of the established prediction models is poor, large errors may occur in the quantitative prediction of mechanical properties using micromagnetic testing instruments.…”

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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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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“…Compared with the MLR method, the neural network is more suitable for dealing with nonlinear modeling problems in quantitative evaluation of yield strength and tensile strength of steels. 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%

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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“…Using a deep RNN, the spatial frequency-sequential relationships for motor imagery classification can even be explored (Luo et al, 2018). Furthermore, the microstructural/mechanical properties and thickness variations in API X65 steel can be determined in a non-destructive way with magnetic hysteresis loop and artificial neural networks (Mirzaee et al, 2020). However, there have been few attempts to intelligently monitor metal materials using Barkhausen noise signals.…”

Section: Introductionmentioning

confidence: 99%

Short communication: A case study of stress monitoring with non-destructive stress measurement and deep learning algorithms

Yao

2022

Mech. Sci.

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Abstract. Non-destructive stress measurement is necessary to provide safety maintenance in some extreme machining environments. This paper reports a case study that reveals the potential application of automatic metal stress monitoring with the aid of the magnetic Barkhausen noise (MBN) signal and deep learning algorithms (convolutional neural network, CNN, and long short-term memory, LSTM). Specifically, we applied the experimental magnetic signals from steel samples to validate the feasibility and efficiency of two deep learning models for stress prediction. The results indicate that the CNN model possesses a faster training speed and a better test accuracy (91.4 %), which confirms the feasibility of automatic stress monitoring applications.

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Non-destructive determination of microstructural/mechanical properties and thickness variations in API X65 steel using magnetic hysteresis loop and artificial neural networks

Cited by 12 publications

References 46 publications

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

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

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

Short communication: A case study of stress monitoring with non-destructive stress measurement and deep learning algorithms

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