Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Sheng, Hongwei; Wang, Ping; Tang, Chenglong

doi:10.3390/ma15062151

Cited by 9 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Magnetic Barkhausen noise was used to measure hot-stamped high strength steel [9] and cold-rolled steel strips [10,11] in a lab setting. These measurements were used to predict different material properties, e.g., yield strength.…”

Section: Non-destructive Testing In Sheet-metal Forming (Rq1)mentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

ORTJOHANN

2023

Materials Research Proceedings

View full text Add to dashboard Cite

Abstract. Material properties can vary both along a sheet-metal coil and from coil to coil despite tight tolerances influencing the process stability of sheet-metal processes and the part quality, which leads to rejects and machine downtime [1]. This significantly affect the economic efficiency of the process. Monitoring fluctuations in material characteristics at regular intervals along sheet-metal coils is enabled directly by non-destructive testing (NDT) before the process offering conclusions on the material properties. Another material monitoring technique arises from monitoring process conditions of the upstream processes, e.g., cold rolling, leading indirectly to insights on material properties. In this work, a systematic literature review (SLR) [2] is conducted to investigate recent approaches for material monitoring and for the utilization of resulting material data for optimizing different sheet-metal forming processes. Existing approaches for different sheet-metal forming processes are critically appraised. Based on the SLR research gaps are revealed and research opportunities, e.g., arising from a potential transfer of existing solutions between different forming processes and recent advances in data-driven methods, are identified.

show abstract

Section: Non-destructive Testing In Sheet-metal Forming (Rq1)mentioning

confidence: 99%

“…Sheet-metal forming in general [1,3,4,20] RQ1 Magnetic barkhausen noise [9][10][11][12] Eddy current [6,7,16,17] 3MA [13][14][15] IMPOC [18,19]…”

Section: Fluctuating Materials Propertiesmentioning

confidence: 99%

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

ORTJOHANN

2023

Materials Research Proceedings

View full text Add to dashboard Cite

show abstract

“…Mechanical properties serve as pivotal indicators reflecting the quality of cold-rolled steel during the rolling production process and act as crucial control parameters. The assessment of these properties typically involves destructive experiments, incurring significant time and resource costs [3]. Moreover, the intricate relationships between mechanical properties, process parameters, and chemical compositions make achieving accurate predictions a challenging endeavor.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Cold-Rolled Steel Based on Improved Graph Attention Network

Luo,

Guo,

Zhang

et al. 2024

Symmetry

View full text Add to dashboard Cite

The prediction of mechanical properties of cold-rolled steel is very important for the quality control, process optimization, and cost control of cold-rolled steel, but it is still a challenging task to predict accurately. For the existing graph structure of graph attention networks, it is difficult to effectively establish the complex coupling relationship and nonlinear causal relationship between variables. At the same time, it is considered that the process of cold-rolled steel has typical full-flow process characteristics and the graph attention network makes it difficult to extract the path information between the central node and its higher-order neighborhood. The neural Granger causality algorithm is used to extract the latent relationship between variables, and the basic graph structure of mechanical property prediction data is constructed. Secondly, the node embedding layer is added before the graph attention network, which leverages the symmetry nature of Node2vec method by incorporating both breadth-first and depth-first exploration strategies. This ensures a balanced exploration of diverse paths in the graph, capturing not only local structures but also higher-order relationships. The combined graph attention networks are then able to effectively capture the symmetry path information between nodes and dependencies between variables. The accuracy and superiority of this method are verified by experiments in real cold-rolled steel production cases.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Cited by 9 publications

References 44 publications

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

Prediction of Mechanical Properties of Cold-Rolled Steel Based on Improved Graph Attention Network

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

Contact Info

Product

Resources

About