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Intelligent representation method of image flatness for cold rolled strip

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Abstract

Real flatness images are the bases for flatness detection based on machine vision of cold rolled strip. The characteristics of a real flatness image are analyzed, and a lightweight strip location detection (SLD) model with deep semantic segmentation networks is established. The interference areas in the real flatness image can be eliminated by the SLD model, and valid information can be retained. On this basis, the concept of image flatness is proposed for the first time. An image flatness representation (IFAR) model is established on the basis of an autoencoder with a new structure. The optimal structure of the bottleneck layer is 16 × 16 × 4, and the IFAR model exhibits a good representation effect. Moreover, interpretability analysis of the representation factors is carried out, and the difference and physical meaning of the representation factors for image flatness with different categories are analyzed. Image flatness with new defect morphologies (bilateral quarter waves and large middle waves) that are not present in the original dataset are generated by modifying the representation factors of the no wave image. Lastly, the SLD and IFAR models are used to detect and represent all the real flatness images on the test set. The average processing time for a single image is 11.42 ms, which is suitable for industrial applications. The research results provide effective methods and ideas for intelligent flatness detection technology based on machine vision.

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References

  1. J. Sun, P.F. Shan, Z. Wei, Y.H. Hu, Q.L. Wang, W. Peng, D.H. Zhang, J. Iron Steel Res. Int. 28 (2021) 563–573.

    Article  Google Scholar 

  2. J. Li, X. Wang, Q. Yang, J. Zhao, Z. Wu, Z. Wang, Int. J. Adv. Manuf. Technol. 123 (2022) 389–405.

    Article  Google Scholar 

  3. D.C. Wang, J. Iron Steel Res. Int. 19 (2012) No. 6, 19–24.

    Article  Google Scholar 

  4. M. Ataka, ISIJ Int. 55 (2015) 89–102.

    Article  Google Scholar 

  5. Y. Zhang, Q. Yang, X.C. Wang, J. Iron Steel Res. Int. 18 (2011) No. 9, 27–32.

    Article  Google Scholar 

  6. Q.L. Wang, J. Sun, X. Li, Y.M. Liu, P.F. Wang, D.H. Zhang, J. Manuf. Process. 34 (2018) 637–649.

    Article  Google Scholar 

  7. J. Sun, S.Z. Chen, Y.L. Wang, X. Lu, Q.L. Wang, D.H. Zhang, J. Iron Steel Res. 34 (2022) 1387–1397.

    Google Scholar 

  8. D.C. Wang, H.M. Liu, J. Liu, Chin. J. Mech. Eng. 30 (2017) 1248–1261.

    Article  Google Scholar 

  9. H. Yu, D. Wang, H. Liu, T. Zhang, L. Yang, ISIJ Int. 60 (2020) 939–947.

    Article  Google Scholar 

  10. H.M. Liu, X.Y. Shan, C.Y. Jia, J. Iron Steel Res. Int. 20 (2013) No. 8, 1–7.

    Article  Google Scholar 

  11. D.L. Xiong, G.H. Zhang, Z.W. Yu, L.J. Chen, X.F. Zhang, H.M. Long, J. Iron Steel Res. 34 (2022) 869–883.

    Google Scholar 

  12. K. Song, Y. Yan, Appl. Surf. Sci. 285 (2013) 858–864.

    Article  Google Scholar 

  13. D. He, K. Xu, P. Zhou, D. Zhou, Opt. Lasers Eng. 117 (2019) 40–48.

    Article  Google Scholar 

  14. G. Fu, P. Sun, W. Zhu, J. Yang, Y. Cao, M.Y. Yang, Y. Cao, Opt. Lasers Eng. 121 (2019) 397–405.

    Article  Google Scholar 

  15. Z.P. Ren, D.G. Li, X.W. Li, Z. Wang, J. Iron Steel Res. 33 (2021) 1118–1126.

    Google Scholar 

  16. Z.Q. Shen, Z.S. Pan, C.X. Cao, J. Zhejiang Univ. 41 (2007) 1615–1619.

    Google Scholar 

  17. B.W. Xu, C. Tang, T. Zhang, J.D. Li, Softw. Eng. 21 (2018) No. 6, 1–3.

    Google Scholar 

  18. G.D. Wang, Z.Y. Liu, D.H. Zhang, M.S. Chu, J. Iron Steel Res. 33 (2021) 1003–1017.

    Google Scholar 

  19. Y. Gan, J. Iron Steel Res. 33 (2021) 997–1002.

    Google Scholar 

  20. Y. Bengio, A. Courville, P. Vincent, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 1798–1828.

    Article  Google Scholar 

  21. G. Shao, X. Chen, X. Zeng, L. Wang, IEEE Trans. Inf. Forensics Secur. 15 (2020) 1525–1540.

    Article  Google Scholar 

  22. P. Sarkar, A. Etemad, IEEE Trans. Affect. Comput. 13 (2022) 1541–1554.

    Article  Google Scholar 

  23. Y. Xu, D. Wang, H. Liu, B. Duan, H. Yu, Steel Res. Int. 93 (2022) 2200284.

    Article  Google Scholar 

  24. S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, D. Terzopoulos, IEEE Trans. Pattern Anal. Mach. Intell. 44 (2022) 3523–3542.

    Google Scholar 

  25. M.H. Hesamian, W. Jia, X. He, P. Kennedy, J. Digit. Imag. 32 (2019) 582–596.

    Article  Google Scholar 

  26. I. Kotaridis, M. Lazaridou, ISPRS J. Photogramm. Remote. Sens. 173 (2021) 309–322.

    Article  Google Scholar 

  27. J. Romero, J.P. Olson, A. Aspuru-Guzik, Quantum Sci. Technol. 2 (2017) 045001.

    Google Scholar 

  28. L. Yang, Z. Zhang, IEEE Trans. Ind. Inform. 17 (2021) 6390–6398.

    Article  Google Scholar 

  29. B. Palsson, M.O. Ulfarsson, J.R. Sveinsson, IEEE Trans. Geosci. Remote. Sens. 59 (2021) 535–549.

    Article  Google Scholar 

  30. K. Zhang, I.M. Bello, Y. Su, J. Wang, I. Maryam, Int. J. Remote. Sens. 43 (2022) 6624–6643.

    Article  Google Scholar 

  31. C.S. Vorugunti, V. Pulabaigari, P. Mukherjee, A. Gautam, Neural Comput. Appl. 34 (2022) 10901–10928.

    Article  Google Scholar 

  32. Y.H. Xu, D.C. Wang, B.W. Duan, H.M. Liu, J. Iron Steel Res. Int. 30 (2023) 994–1012.

    Article  Google Scholar 

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Acknowledgements

This project is supported by the National Natural Science Foundation of China (No. U21A20118) and the National Key Laboratory of Metal Forming Technology and Heavy Equipment, China National Heavy Machinery Research Institute Co.,Ltd. (No. S2208100.W04).

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Authors and Affiliations

  1. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Yang-huan Xu, Dong-cheng Wang, Hong-min Liu & Bo-wei Duan

  2. National Key Laboratory of Metal Forming Technology and Heavy Equipment, China National Heavy Machinery Research Institute Co., Ltd., Xi’an, 710018, Shaanxi, China

    Dong-cheng Wang

  3. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Dong-cheng Wang & Hong-min Liu

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  1. Yang-huan Xu
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  2. Dong-cheng Wang
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  3. Hong-min Liu
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  4. Bo-wei Duan
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Correspondence to Dong-cheng Wang.

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Xu, Yh., Wang, Dc., Liu, Hm. et al. Intelligent representation method of image flatness for cold rolled strip. J. Iron Steel Res. Int. 31, 1177–1195 (2024). https://doi.org/10.1007/s42243-023-01068-3

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  • DOI: https://doi.org/10.1007/s42243-023-01068-3

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