1 Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University, Shenyang 110819, Liaoning, China 2 State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China 3 Shandong Iron and Steel Group Co., Ltd., Jinan 271105, Shandong, China 4 HBIS Material Technology Research Institude, Shijiazhuang 050023, Hebei, China 5 Special Steel Business Department of Shandong Steel Laiwu Branch, Jinan 271105, Shandong, China
Automatic recognition and intelligent analysis of central shrinkage defects of continuous casting billets based on deep learning
1 Key Laboratory of Electromagnetic Processing of Materials (Ministry of Education), Northeastern University, Shenyang 110819, Liaoning, China 2 State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China 3 Shandong Iron and Steel Group Co., Ltd., Jinan 271105, Shandong, China 4 HBIS Material Technology Research Institude, Shijiazhuang 050023, Hebei, China 5 Special Steel Business Department of Shandong Steel Laiwu Branch, Jinan 271105, Shandong, China
摘要 The internal quality inspection of the continuous casting billets is very important, and mis-inspection will seriously affect the subsequent production process. The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets. The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9. We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length, width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets. The results show that all the testing images are rated correctly, and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.
Abstract：The internal quality inspection of the continuous casting billets is very important, and mis-inspection will seriously affect the subsequent production process. The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets. The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9. We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length, width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets. The results show that all the testing images are rated correctly, and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.
Gong-hao Lian,Qi-hao Sun,Xiao-ming Liu, et al. Automatic recognition and intelligent analysis of central shrinkage defects of continuous casting billets based on deep learning[J]. Journal of Iron and Steel Research International, 2023, 30(05): 937-948.
2023, Vol. 30 
