CFRP Impact Damage Inspection Based on Manifold Learning Using Ultrasonic Induced Thermography

Abstract

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging nondestructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high-temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this paper, an impact damage inspection method is proposed based on manifold learning for the CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as datasets in high-dimensional space. These datasets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable, and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.