XU An-tao,ZHANG Zhen-nan,ZHANG Rui,SUN Bo.Corrosion Behavior of Military Grey Coating Based on Self-organizing Feature Mapping (SOFM)[J],46(10):241-246
Corrosion Behavior of Military Grey Coating Based on Self-organizing Feature Mapping (SOFM)
Received:April 12, 2017  Revised:October 20, 2017
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DOI:10.16490/j.cnki.issn.1001-3660.2017.10.036
KeyWord:organic coating  complete immersion experiment  electrochemical impedance spectroscopy  SOFM self-org- anizing feature mapping
           
AuthorInstitution
XU An-tao a. Military Vehicle Department, Military Transportation University, Tianjin , China
ZHANG Zhen-nan b. Postgraduate Training Brigade, Military Transportation University, Tianjin , China
ZHANG Rui b. Postgraduate Training Brigade, Military Transportation University, Tianjin , China
SUN Bo Unit 97274, Luoyang , China
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Abstract:
      The work aims to explore corrosion behavior characteristics of grey organic coating on military vehicles in complete immersion state, and look for effective methods of evaluating corrosion protection coating performance. Corrosion behavior of a certain military grey coating in complete immersion state was studied in EIS method, impedance spectroscopy as well as change rules of three characteristic parameters, i.e., low frequency impedance , high frequency phase angle and relative dielectric constant were studied. With the three characteristic parameters as evaluation indices, changing process of coating properties was studied by using self-organizing feature mapping. Corrosion process of the grey coating in complete immersion state has undergone three stages—good stage: high frequency phase angle was near 70°, and low frequency impedance was over 107; protective properties degraded but still played a protective role: high frequency phase angle dropped to near 50°, and low frequency impedance dropped to near 106; and protective performance loss stage: high frequency phase angle was below 50°, and low frequency impedance was below 105. Coating classification results of SOFM self-organizing neural network were consistent with those of impedance spectroscopy analysis. Case analysis indicates that coating performance state can be judged quickly in SOFM method.
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