Sub-surface defect detection in a steel sheet

被引:27
作者
Atzlesberger, J. [1 ]
Zagar, B. G. [1 ]
Cihal, R. [2 ]
Brummayer, M. [2 ]
Reisinger, P. [2 ]
机构
[1] Johannes Kepler Univ Linz, Inst Measurement Technol, A-4040 Linz, Austria
[2] Voestalpine Stahl GmbH, Steel Plant Business Unit Hot Strip Prod, Res & Dev, Linz, Austria
关键词
non-destructive testing (NDT); magnetic flux leakage (MFL); giant magnetoresistance (GMR); defect detection;
D O I
10.1088/0957-0233/24/8/084003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In recent years, the focus on quality control in the steel industry has shifted from offline to inline non-destructive testing in order to detect defects at the earliest possible stage in the production process. The detection and elimination of such defects is vital for sustaining product quality and reducing costs. Various measurement principles (e.g. ultrasonic testing, electromagnetic acoustic transducer, x-ray inspection) were analyzed and their advantages and disadvantages are discussed regarding their usability in a steel plant. Based on these findings a magnetic method combined with a new sensor concept was chosen. By using highly sensitive sensors based on the giant magnetoresistive effect, it is possible to detect magnetic flux leakage variations on the surface of a magnetized steel strip caused by defects or inhomogeneities inside the material. Based on promising measurement results of preliminary tests and simulation results obtained by finite element method-models, a prototype is now being built for offline measurements and the optimization of the measurement method. In the event that the development of this second prototype is successful, an inline configuration will be implemented.
引用
收藏
页数:8
相关论文
共 50 条
[31]   Steel surface defect detection algorithm based on ESI-YOLOv8 [J].
Zhang, Xinrong ;
Wang, Yanlong ;
Fang, Huaisong .
MATERIALS RESEARCH EXPRESS, 2024, 11 (05)
[32]   Improved YOLOv7-based steel surface defect detection algorithm [J].
Xie, Yinghong ;
Yin, Biao ;
Han, Xiaowei ;
Hao, Yan .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2024, 21 (01) :346-368
[33]   Improved Steel Surface Defect Detection Algorithm Based on YOLOv8 [J].
You, Congzhe ;
Kong, Haozheng .
IEEE ACCESS, 2024, 12 :99570-99577
[34]   Enhanced Faster Region Convolutional Neural Networks for Steel Surface Defect Detection [J].
Wei, Rubo ;
Song, Yonghong ;
Zhang, Yuanlin .
ISIJ INTERNATIONAL, 2020, 60 (03) :539-545
[35]   An Improved YOLOv8 Model for Strip Steel Surface Defect Detection [J].
Wang, Jinwen ;
Chen, Ting ;
Xu, Xinke ;
Zhao, Longbiao ;
Yuan, Dijian ;
Du, Yu ;
Guo, Xiaowei ;
Chen, Ning .
APPLIED SCIENCES-BASEL, 2025, 15 (01)
[36]   Steel Surface Defect Detection Using GAN and One-Class Classifier [J].
Liu, Kun ;
Li, Aimei ;
Wen, Xi ;
Chen, Haiyong ;
Yang, Peng .
2019 25TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC), 2019, :595-600
[37]   Surface defect detection of steel strip based on spectral residual visual saliency [J].
Chen H.-Y. ;
Xu S. ;
Liu K. ;
Sun H.-X. .
Chen, Hai-Yong (haiyong.chen@hebut.edu.cn), 1600, Chinese Academy of Sciences (24) :2572-2580
[38]   Lightweight Network-Based Surface Defect Detection Method for Steel Plates [J].
Wang, Changqing ;
Sun, Maoxuan ;
Cao, Yuan ;
He, Kunyu ;
Zhang, Bei ;
Cao, Zhonghao ;
Wang, Meng .
SUSTAINABILITY, 2023, 15 (04)
[39]   Steel Surface Defect Detection Technology Based on YOLOv8-MGVS [J].
Zeng, Kai ;
Xia, Zibo ;
Qian, Junlei ;
Du, Xueqiang ;
Xiao, Pengcheng ;
Zhu, Liguang .
METALS, 2025, 15 (02)
[40]   A Defect Detection Method based on Sub-image Statistical Feature for Texture Surface [J].
Wu, Xiaojun ;
Xiong, Huijiang ;
Wen, Peizhi .
EIGHTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2016), 2016, 10033