4.0 Article

A New Method for Adhesion Strength Assessment of Indented Polymer Coatings

期刊

PHYSICAL MESOMECHANICS
卷 26, 期 5, 页码 514-522

出版社

SPRINGER
DOI: 10.1134/S1029959923050041

关键词

coating; indentation; adhesion strength; finite element simulation; cohesive zone model; specific surface energy of adhesive failure

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The paper proposes a new method for assessing the adhesion strength of polymer coatings using Rockwell indentation and finite element simulation. The method involves the processing of experimental data through fracture mechanics analysis. The study demonstrates that when the Rockwell indenter penetrates perpendicular to the coating surface, circular buckling delamination occurs due to adhesive bond rupture by radial shear.
The paper proposes a new method for adhesion strength assessment of polymer coatings which is based on Rockwell indentation with experimental data processing via finite element simulation in terms of fracture mechanics (cohesive zone model, CZM). With the example of a titanium-alkoxide epoxy composition deposited on low-carbon steel, it is shown that when the Rockwell indenter penetrates perpendicular to the coating surface, circular buckling delamination around its indent occurs due to adhesive bond rupture by radial shear with extrusion of the coating material from beneath the indenter. The parameter controlled in the simulation is the width of coating delamination zones formed in indentation experiments at a constant indentation depth. The conditions of adhesive contact are specified using the CZM bilinear law, which describes the relation between the tangential adhesive stress and the adhesive bond elongation under shear in the contact plane of interacting surfaces. The criterion of quantitative adhesion strength assessment is the ultimate specific surface energy of adhesive failure. The simulation gives an optimum value of the ultimate specific surface energy of adhesive failure of the coating at CZM parameters that provide the best convergence of the numerical and experimental data.

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