ArticleMaterials (Basel, Switzerland)2022
Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Micromagnetic and Quantitative Prediction of Hardness and Impact Energy in Martensitic Stainless Steels Using Mutual Information Parameter Screening and Random Forest Modeling Methods.Materials (Basel, Switzerland) · 2025Article
- Surface Crack Detection of Aluminum Alloy Using Injected Direct Current-Magnetic Field Measurement Method.Sensors (Basel, Switzerland) · 2025Article
- Stress and Microstructures Characterization Based on Magnetic Incremental Permeability and Magnetic Barkhausen Noise Techniques.Materials (Basel, Switzerland) · 2024Article
- A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring.Sensors (Basel, Switzerland) · 2023Review
- Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls' motion properties. Pearson's correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.
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Registered trials
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