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Weldment Nondestructive Testing Using Magneto-optical Imaging Induced by Alternating Magnetic Field

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Abstract

This paper introduces an innovative Nondestructive testing (NDT) approach by using dynamic magneto-optical imaging (MOI) system to diagnose weld defects. MOI mechanism was explained by Faraday magneto-optical effect and magnetic domain theory. Two Q235 specimen MOI experiments based on excitation of permanent magnet and alternating electromagnet (alternating current driven electromagnet) were performed, thus the feasibility of MOI system for weld defects detection was verified and the relation between alternating magnetic field (AMF) and dynamic MO images was discussed as well. In this research, AMF of welded high-strength steel (HSS) weldment was excited by an alternating electromagnet, and dynamic MO images of HSS seam were acquired for weldment NDT. Finally, a pattern recognition method including three steps was proposed. Dynamic MO images were fused periodically and the features of fused images were extracted by principal component analysis. A classifier based on error back propagation (BP) neural network was established to identify these weld features. It proved that typical weld features such as incomplete penetration, sag, crack and no defect can be classified by the proposed method with an accuracy of 93.5%.

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References

  1. Runnemalm, A., Broberg, P., Henrikson, P.: Ultraviolet excitation for thermography inspection of surface cracks in welded joints. Nondestruct. Test. Eva. 29(29), 332–344 (2014)

    Article  Google Scholar 

  2. Zapata, J., Vilar, R., Ruiz, R.: Automatic inspection system of welding radiographic images based on ANN under a regularisation process. J. Nondestruct. Eval. 31(1), 34–45 (2012)

    Article  Google Scholar 

  3. Cong, S., Gang, T., Zhang, J., Sheng, C.: Parameter design of linear frequency modulated excitation waveform for ultrasonic nondestructive testing of metallic materials. J. Nondestruct. Eval. 33(4), 684–693 (2014)

    Article  Google Scholar 

  4. Mahmoudi, A., Regragui, F.: Synthetic minority oversampling and linear cross-validated support vector machine-based recursive feature elimination to classify weld flaws in radiographic images. Mater. Eval. 73, 186–197 (2015)

    Google Scholar 

  5. Postolache, O., Ramos, H.G., Ribeiro, A.L.: Detection and characterization of defects using GMR probes and artificial neural networks. Comput. Stand. Interfaces 33, 191–200 (2011)

    Article  Google Scholar 

  6. Tsukada, K., Miyake, K., Harada, D., Sakai, K., Kiwa, T.: Magnetic nondestructive test for resistance spot welds using magnetic flux penetration and eddy current methods. J. Nondestruct. Eval. 32(3), 286–293 (2013)

    Article  Google Scholar 

  7. Deng, Y., Liu, X., Udpa, L.: Magneto-optic imaging for aircraft skins inspection: a probability of detection study of simulated and experimental image data. IEEE T. Reliab. 61, 1–8 (2011)

    Google Scholar 

  8. Gao, X., Chen, Y.: Detection of micro gap weld using magneto-optical imaging during laser welding. Int. J. Adv. Manuf. Technol. 73, 23–33 (2014)

    Article  Google Scholar 

  9. Gao, X., Mo, L., Xiao, Z., Chen, X., Katayama, S.: Seam tracking based on Kalman filtering of micro-gap weld using magneto-optical image. Int. J. Adv. Manuf. Technol. 83, 21–32 (2016)

    Article  Google Scholar 

  10. Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: Multi-focus image fusion for visual sensor networks in DCT domain. Comput. Electr. Eng. 37(5), 789–797 (2011)

    Article  MATH  Google Scholar 

  11. Prakash, O., Kumar, A., Khare, A.: Pixel-level image fusion scheme based on steerable pyramid wavelet transform using absolute maximum selection fusion rule. In: International Conference on Issues and Challenges in Intelligent Computing Techniques (pp. 765–770). IEEE (2014)

  12. Feldman, A., Brower, W.S., Horowitz, D.: Optical activity and faraday rotation in bismuth oxide compounds. Appl. Phys. Lett. 16, 201–202 (1970)

    Article  Google Scholar 

  13. Murakami, H., Tonouchi, M.: High-sensitive scanning laser magneto-optical imaging system. Rev. Sci. Instrum. 81(1), 013701 (2010)

    Article  Google Scholar 

  14. Radtke, U., Zielke, R., Rademacher, H.G., Crostack, H.A., Hergt, R.: Application of magneto-optical method for real-time visualization of eddy currents with high spatial resolution for nondestructive testing. Opt. Laser. Eng. 36, 251–268 (2001)

    Article  Google Scholar 

  15. Weiss, P.: Molecular field and ferromagnetic property. J. Phys. Theor. Appl. 6, 661–690 (1907)

    Article  Google Scholar 

  16. Chikazumi, S., Graham, C.D.: Physics of Ferromagnetism, pp. 16–20. Oxford University Press, Oxford (2009)

    Google Scholar 

  17. Sudo, S., Asano, D., Takana, H., Nishiyama, H.: The dynamic behavior of magnetic fluid adsorbed to small permanent magnet in alternating magnetic field. J. Magn. Magn. Mater. 323, 1314–1318 (2015)

    Article  Google Scholar 

  18. Bouchala, T., Abdelhadi, B., Benoudjit, A.: Application of coupled electric field method for eddy current non-destructive inspection of multilayer structures. Nondestruct. Test. Eva. 30, 124–137 (2015)

    Article  Google Scholar 

  19. Richert, H., Schmidt, H., Lindner, S., Lindner, M., Wenzel, B., Holzhey, R.: Dynamic magneto-optical imaging of domains in grain-oriented electrical steel. Steel. Res. Int. 9999, 1–9 (2015)

    Google Scholar 

  20. Chen, G., Zhu, J., Li, J., Liu, F.Z., Wu, Y.Z.: Revealing the volume magnetic anisotropy of Fe films epitaxied on GaAs(001) surface. Appl. Phys. Lett. 98, 132505 (2011)

    Article  Google Scholar 

  21. Muduli, P.K., Rice, W.C., He, L., Collins, B.A., Chu, Y.S., Tsui, F.: Study of magnetic anisotropy and magnetization reversal using the quadratic magneto optical effect in epitaxial Co(x)mn(y)Ge(z)(111) films. J. Phys-Condens. Mat. 21, 296005–296015 (2009)

    Article  Google Scholar 

  22. You, D., Gao, X., Katayama, S.: WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE T. Ind. Electron. 62, 628–636 (2015)

    Article  Google Scholar 

  23. Rumelhart, D., Hinton, G., William, R.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China [Grant No. 51675104], the Science and Technology Planning Project of Guangzhou, China [Grant No. 201510010089], and the Science and Technology Planning Public Project of Guangdong Province, China [Grant No. 2016A010102015].

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Correspondence to Xiangdong Gao.

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Gao, X., Lan, C., You, D. et al. Weldment Nondestructive Testing Using Magneto-optical Imaging Induced by Alternating Magnetic Field. J Nondestruct Eval 36, 55 (2017). https://doi.org/10.1007/s10921-017-0434-4

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