Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

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Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.