Nondestructive detection of laser-cladding coating interface defects: a deep learning-enhanced laser ultrasonics approach

Abstract

ABSTRACT Laser-cladding technology is now widely used in repairing mechanical parts and creating functional coatings. However, interface defects are commonly found in laser-cladding coatings, significantly impacting the fatigue and mechanical properties of these coatings. Therefore, it is necessary to detect interface defects in laser-cladding coatings. In this study, we utilised laser ultrasonics (LU) method to test interface defects non-destructively. However, the signal-to-noise ratio (SNR) of LU signals was poor. To address this issue, an unsupervised deep learning method was used to enhance the SNR. The results showed that the noises were suppressed by the deep learning method. Based on the deep learning-enhanced LU method, surface acoustic waves were used to present B-scan and C-scan images. The artificial defects with a 0.5 mm diameter were detected. Consequently, the deep learning-enhanced LU method proves to be a viable approach for the contactless, online, and non-destructive testing of interface defects in laser-cladding coatings.