Based on the accuracy requirements for the thickness of the polyurethane form layer on the outer surface of the fuel tank, the calibration testing of four common curvature section specimens is conducted. Besides, the eddy current ranging calibration method based on support vector machine regression analysis is proposed to establish the lifting distance prediction model (LDPM), which is used for curved surface eddy current distance measurement. The influence of curvature on the measurement errors of eddy current ranging is studied. The measurement errors are divided into the surface curvature errors and other errors according to the cause of the error. In the different ranges of the sensor range, the relative variation of the error components is analyzed, which provides a basis for surface measurement error compensation. In addition, the comparative analysis of LDPM and the calibration function obtained by using common calibration methods in terms of measurement accuracy and calculation rate are performed, which provides suggestions for the selection of the calibration method for eddy current ranging of curved parts. The results show that the surface curvature error is not sensitive to the curvature in the initial stage of the sensor range. Near the midpoint of the range, the absolute value of the surface curvature error decreases first and then increases with the increase of curvature. In the terminal region, the absolute value of the surface curvature error increases with the rise of curvature, while other errors remain constant within different measurement ranges over the entire range. Moreover, the LDPM can control the measurement error within ［-0.5, 0.5］mm, and the accuracy is comparable to five multi-peak Gaussian fitting, higher than the fourth-order polynomial fitting, which can meet the thickness measurement accuracy requirements and is convenient for non-destructive testing.
TAO Zhengrui, DANG Jiaqiang, XU Jinyang, AN Qinglong, CHEN Ming, WANG Li, REN Fei
. Eddy Current Distance Measurement Calibration Method for Curved
Surface Parts Based on Support Vector Machine Regression[J]. Journal of Shanghai Jiaotong University, 2020
, 54(7)
: 674
-681
.
DOI: 10.16183/j.cnki.jsjtu.2020.99.010
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