The nondestructive characterization of coating thickness, acoustic velocity, and density is essential for industrial quality control. However, conventional ultrasonic reflection coefficient amplitude spectrum (URCAS)-based inversion methods typically require prior knowledge of acoustic attenuation, which is often unavailable for thin coatings and limits their practical applicability. To address this issue, a nonlinearity-guided dual-spectrum inversion framework is proposed by combining the URCAS with a layer-phase spectrum. It is found that the layer-phase spectrum exhibits strong nonlinear sensitivity to variations in acoustic velocity and density, which helps improve parameter identifiability. Based on this property, an improved particle swarm optimization algorithm is developed to enable simultaneous inversion of thickness, velocity, and density without explicit prior attenuation information. Finite-element simulations show that the conventional URCAS method yields mean relative errors exceeding 5%, whereas the proposed method reduces these errors to below 3% under the tested conditions. Experimental validation on eight industrial polytetrafluoroethylene (PTFE) coatings with thicknesses ranging from 20.89 μm to 120.11 μm (down to approximately 0.11 λ at 10 MHz) demonstrates that the proposed method achieves average relative errors within 10% and improves inversion accuracy by about 6% compared with amplitude-only approaches. The results indicate that the proposed attenuation-independent and nonlinearity-guided strategy provides an effective solution for the quantitative nondestructive evaluation of subwavelength coatings. The method is particularly suitable for thin coatings with unknown attenuation.
WANG et al. (Sun,) studied this question.