Solving the Inverse Heat Conduction Problem in Using Long Square Pulse Thermography to Estimate Coating Thickness by Using SVR Models Based on Restored Pseudo Heat Flux (RPHF) In-Plane Profile Academic Article uri icon


  • 2018, Springer Science+Business Media, LLC, part of Springer Nature. This research develops a method to estimate opaque coating thickness based on time-and-space-resolved thermography. Thermography is a viable technique for measuring coating thickness. However, time-resolved thermography becomes unreliable when uncontrolled constant thermal stimulation amplitude appears. Therefore, a time- and space-resolved thermography technique for coating thickness measurement called restored pseudo heat flux (RPHF) has been developed by using FourierHankel transform. A non-dimensional analysis was conducted and the results show RPHF curves with different coating thicknesses converging as the thermal diffusivity ratio or thermal conductivity ratio goes to one. For large thermal conductivity ratio values, the RPHF curves have two inflection points along the non-dimensional radius. Fifty-nine samples were tested using the proposed method. Support vector regression (SVR) models were constructed with the in-plane distribution of RPHF and the temporal distribution of the measured surface temperature as inputs. To avoid overfitting, cross validation was applied to all the models. Later, another twenty-eight samples were tested to validate the SVR models. The results suggest that a support vector regression model with in-plane profiles of RPHF handles uncontrolled heat flux variation better and yields a better performance in coating thickness measurement than the temporal profile does. With in-plane RPHF profile as input, the SVR model can evaluate coating thickness with a relative root mean square error at 25.3% even when heat amplitude varies 50%.

published proceedings


altmetric score

  • 0.5

author list (cited authors)

  • Wang, H., & Hsieh, S.

citation count

  • 2

complete list of authors

  • Wang, Hongjin||Hsieh, Sheng-Jen

publication date

  • December 2018