Skip to main content
SpringerLink
Log in

Quantitative Estimation of Fe-Based Amorphous Coating Thickness Based on Pulsed Eddy Current Technology

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

Fe-based amorphous coatings prepared by high-velocity oxy-fuel (HVOF) spraying have the advantages of good mechanical properties, high density, low porosity, and high amorphous content. The service life and bonding strength of coating greatly depend on its thickness; however, the characterization of ferromagnetic coating thickness is a very difficult problem. Pulsed eddy current (PEC) is characterized by abundant signals in frequency domains. In this paper, the thickness measurement principle of ferromagnetic coating was explored, and the typical and entropy features from PEC signals were extracted. Seven integrated learning methods were combined to quantitatively characterize the coating thickness, namely ridge regression (RR), lasso regression (LR), random forest regression (RFR), extra trees regression (ETR), gradient boosting tree regression (GBTR), addaptive boost regression (ABR) and eXtreme Gradient Boosting Regression (XGBR) algorithms. By comparing typical features with new ones, it was verified that the effective combination of entropy features and typical features could be used as effective feature parameters of eddy current signal. Statistical scores (RMSE and R2) and GridsearchCV features were used to evaluate and optimize the established model. As indicated by the results, the proposed XGBR machine learning model well predicted the coating thickness, and the relative error less than 0.05 mm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of Data and Material

Not applicable.

References

  1. Lu, W., Wang, D., Wang, Q., Yang, F., Li, T., Shi, Y., Zhang, S., Yang, B.: Sensitivity of corrosion behavior for Fe-based amorphous coating to temperature and chloride concentration. Coatings 11(3), 331 (2021)

    Article  Google Scholar 

  2. Vignesh, S., Shanmugam, K., Balasubramanian, V., Sridhar, K., Thirumalaikumarasamy, D.: Electrochemical corrosion behaviour of HVOF sprayed iron-based amorphous metallic coatings on AISI 316 stainless steel in an NaCl solution. J. Mech. Behav. Mater. 27(3–4), 20180015 (2018)

    Google Scholar 

  3. Yasir, M., Zhang, C., Wang, W., Zhang, Z.W., Liu, L.: Tribocorrosion behavior of Fe-based amorphous composite coating reinforced by Al2O3 in 3.5% NaCl solution. J. Therm. Spray Technol. 25(8), 1554–60 (2016)

    Article  Google Scholar 

  4. Hou, G., Zhao, X., Zhou, H., Lu, J., An, Y., Chen, J., Yang, J.: Cavitation erosion of several oxy-fuel sprayed coatings tested in deionized water and artificial seawater. Wear 311(1–2), 81–92 (2014)

    Article  Google Scholar 

  5. Peng, Y., Zhang, C., Zhou, H., Liu, L.: On the bonding strength in thermally sprayed Fe-based amorphous coatings. Surf. Coat. Technol. 218, 17–22 (2013)

    Article  Google Scholar 

  6. Nguyen, V.P., Dang, T.N., Le, C.C., Wang, D.A.: Effect of coating thickness on fatigue behavior of AISI 1045 steel with HVOF thermal spray and hard chrome electroplating. Journal of Thermal Spray Technology. 29(8), 1968–81 (2020)

    Article  Google Scholar 

  7. Ranjit, S., Chung, Y., Kim, W.: Thermal behavior variations in coating thickness using pulse phase thermography. J. Korean Soc. Nondestr. Test. 36(4), 259–265 (2016)

    Article  Google Scholar 

  8. Zhang, J., Yuan, M., Song, S.-J., Kim, H.-J.: Precision measurement of coating thickness on ferromagnetic tube using pulsed eddy current technique. Int. J. Precis. Eng. Manuf. 16(8), 1723–1728 (2015)

    Article  Google Scholar 

  9. Wang, H., Li, W., Feng, Z.: Noncontact thickness measurement of metal films using eddy-current probes immune to distance variation. IEEE Trans. Instrum. Meas. 64(9), 2557–2564 (2014)

    Article  Google Scholar 

  10. Wang, Y., Fan, M., Cao, B., Ye, B., Wen, D.: Measurement of coating thickness using lift-off point of intersection features from pulsed eddy current signals. NDT and E Int. 116, 102333 (2020)

    Article  Google Scholar 

  11. Abdou, A., Bouchala, T., Abdelhadi, B., Guettafi, A., Benoudjit, A.: Nondestructive eddy current measurement of coating thickness of aeronautical construction materials. Instrum. Meas. Métrol. 18(5), 451–457 (2019)

    Google Scholar 

  12. Cheng, Y., Chen, Y., Jiang, J., Bai, L., Zhang, B.: Absorbing coating thickness measurement based on lift-off effect of eddy current testing. Int. J. Appl. Electromagn. Mech. 45(1–4), 323–330 (2014)

    Article  Google Scholar 

  13. Li, Y., Chen, Z., Mao, Y., Qi, Y.: Quantitative evaluation of thermal barrier coating based on eddy current technique. NDT and E Int. 50, 29–35 (2012)

    Article  Google Scholar 

  14. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Visi. Graph. Image Process. 29, 273–285 (1985)

    Article  Google Scholar 

  15. Hosseini, S.M., Ghasemi-Ghalebahman, A., Azadi, M., Jafari, S.M.: Crack initiation detection in crankshaft ductile cast iron based on information entropy of acoustic emission signals under tensile loading. Eng. Fail. Anal. 127, 105547 (2021)

    Article  Google Scholar 

  16. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  17. Chen, G., Cao, Z.: Quantitative evaluation of eddy current distribution by relative entropy and cross entropy. Meas. Control 54(3–4), 164–169 (2021)

    Article  Google Scholar 

  18. Liu, Z., Yao, J., He, C., Li, Z., Liu, X., Wu, B.: Development of a bidirectional-excitation eddy-current sensor with magnetic shielding: detection of subsurface defects in stainless steel. IEEE Sens. J. 18(15), 6203–16 (2018)

    Article  Google Scholar 

  19. Xu, C., Liu, X., Wang, H., Li, Y., Jia, W., Qian, W., Quan, Q., Zhang, H., Xue, F.: A study of predicting irradiation-induced transition temperature shift for RPV steels with XGBoost modeling. Nucl. Eng. Technol. 53(8), 2610–2615 (2021)

    Article  Google Scholar 

  20. Behmann, J., Mahlein, A.-K., Rumpf, T., Römer, C., Plümer, L.: A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agric. 16(3), 239–260 (2015)

    Article  Google Scholar 

  21. Bernieri, A., Ferrigno, L., Laracca, M., Molinara, M.: Crack shape reconstruction in eddy current testing using machine learning systems for regression. IEEE Trans. Instrum. Meas. 57(9), 1958–1968 (2018)

    Article  Google Scholar 

  22. Banerjee, P., Udpa, L., Udpa, S., Benson, J.: Confidence metric for signal classification in non destructive evaluation. NDT & E Int. 91, 88–96 (2017)

    Article  Google Scholar 

  23. Vilar, R., Zapata, J., Ruiz, R.: An automatic system of classification of weld defects in radiographic images. NDT & E Int. 42(5), 467–476 (2009)

    Article  Google Scholar 

  24. Boaretto, N., Centeno, T.M.: Automated detection of welding defects in pipelines from radiographic images DWDI. Ndt & E Int. 86, 7–13 (2017)

    Article  Google Scholar 

  25. Porta, A., Baselli, G., Liberati, D., Montano, N., Cogliati, C., Gnecchi-Ruscone, T., Malliani, A., Cerutti, S.: Measuring regularity by means of a corrected conditional entropy in sympathetic outflow. Biol. Cybern. 78(1), 71–78 (1998)

    Article  MATH  Google Scholar 

  26. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Article  Google Scholar 

  27. Chen, W., Wang, Z., Xie, H., Yu, W.: Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 15(2), 266–272 (2007)

    Article  Google Scholar 

  28. Bounoua, W., Benkara, A.B., Kouadri, A., Bakdi, A.: Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection. Trans. Inst. Meas. Control. 42(6), 1225–1238 (2020)

    Article  Google Scholar 

  29. Gong, Y., Huang, X., Liu, Z., Deng, F., Wu, Y., He, C.: Development of a cone-shaped pulsed eddy current sensor. IEEE Sens. J. 22(4), 3129–36 (2022)

    Article  Google Scholar 

  30. Panzone, L., Ulph, A., Areal, F., Grippo, V.: A ridge regression approach to estimate the relationship between landfill taxation and waste collection and disposal in England. Waste Manag. 129, 95–110 (2021)

    Article  Google Scholar 

  31. Yang, X., Wen, W.: Ridge and Lasso regression models for cross-version defect prediction. IEEE Trans. Reliab. 67(3), 885–896 (2018)

    Article  Google Scholar 

  32. Zhang, R., Li, Y., Goh, A.T., Zhang, W., Chen, Z.: Analysis of ground surface settlement in anisotropic clays using extreme gradient boosting and random forest regression models. J. Rock Mech. Geotech. Eng. 13(6), 1478–84 (2021)

    Article  Google Scholar 

  33. Djarum, D., Ahmad, Z., Zhang, J.: River water quality prediction in Malaysia based on extra tree regression model coupled with linear discriminant analysis (LDA). Computer Aided Chemical Engineering 50, 1491–1496 (2021)

    Article  Google Scholar 

  34. Yang, F., Wang, D., Xu, F., Huang, Z., Tsui, K.: Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model. J. Power Sources 476, 228654 (2020)

    Article  Google Scholar 

  35. Koduri, S.B., Gunisetti, L., Ramesh, C.R., Mutyalu, K.V., Ganesh, D.: Prediction of crop production using adaboost regression method. J. Phys: Conf. Ser. 1228, 012005 (2019)

    Google Scholar 

Download references

Acknowledgements

This study was supported in part by the National Key Research and Development Program of China under Grant 2018YFC1902404, in part by the National Natural Science Foundation of China under Grant 11772014.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China

    Xiaofei Huang, Zenghua Liu, Yu Gong, Sha Wu, Kewei Chen & Cunfu He

Authors
  1. Xiaofei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zenghua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sha Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kewei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cunfu He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XH and ZL wrote the main manuscript text. Sha Wu prepared Fig. 5. KC prepared Figs. 3–4. YG and CH participated in the discussion. All authors reviewed the manuscript.

Corresponding author

Correspondence to Zenghua Liu.

Ethics declarations

Competing interests

The authors have declared that no competing interests exist.

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Published informed consent were obtained from all participants.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, X., Liu, Z., Gong, Y. et al. Quantitative Estimation of Fe-Based Amorphous Coating Thickness Based on Pulsed Eddy Current Technology. J Nondestruct Eval 42, 1 (2023). https://doi.org/10.1007/s10921-022-00912-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-022-00912-y

Keywords

Search

Navigation