Today the infrared thermography is among the nondestructive testing methods (NDT) most used for detection and characterization of internal defects in materials. It has become a reference method in industrial installations control. As the interpretation of thermal images provided by the infrared cameras is often difficult; therefore, it is necessary; to seek new methods fast and reliable for intelligent nondestructive evaluation. In our work we propose a fast method using artificial neural networks for internal defects depth evaluation from the thermal contrast. Experimental results have confirmed the method efficiency in predicting the defects depths.

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The use of coatings is an important tool in the industry. It allows protecting against oxidation, corrosion and various types of fatigue. The coating thickness is an important characteristic that influences the quality and the performance of materials. In this paper, we develop a simple method of infrared lock-in thermography (LIT) to determine galvanizing coating thickness measurement, by using a sample multiple zinc layer with thickness ranging from 0.25[Formula: see text]mm to 1.5[Formula: see text]mm. The method has the particularity of taking a sinusoidal excitation heat flux which contributes with a heat exchange coefficient fixed at 10[Formula: see text]w/m2k and a surface emissivity of about 0.1. The finite element method (FEM) is used to model and analyze the thermal response of studied structure. The metal substrate used in this study is a structural steel, covered with six zinc layers. The finite elements analysis allows us to determine the temperature evolution at different points on the specimen. The Fourier transform method is used on the Matlab software to determine the phase angle of the data found. A correlation between the coating thickness and the equivalent phase angle is defined, and the results deduced show that the estimated values are close to the actual coating thicknesses with a precision ranging from 0.029[Formula: see text]mm to 0.011[Formula: see text]mm.