# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 1 Introduction  
Laser-engineered net shaping (LENS) is a metal additive manufacturing technique that enables laser repair (LR) of industrial components, in which metallic powders are melted by high energy laser, and consecutive layers are additively deposited. LENS has become one of the most important repairing methods for damaged components, considering its advantages in free forming, automation and high efficiency [1; 2; 3; 4; 5; 6]. The laser-repaired components generally consist of the repair zone (containing the cladding and fusion zones), the heat-affected zone and the substrate. The defects and their distribution characteristics in the repaired components differ significantly from those using conventional metal forming processes, especially in the repair zone, where multiple types of defects may exist [1; 5]. It has been shown that the repaired components are prone to generate surface cracks, which can seriously affect the repair quality and even leads to the mechanical failure of the repaired components. Therefore, the nondestructive testing of surface cracks is required to guarantee the quality and structural integrity of repaired components, a challenge which receives much attention from industry and academia [7; 8; 9; 10]. In the detection of surface cracks, thermography methods show great potential due to their advantages of being non-contact and full-field [11; 12; 13; 14; 15; 16]. Compared with traditional active thermography techniques, laser thermography retains the characteristics of being non-contact and full-field, and also has the advantage of high detection accuracy. It has a good application potential in nondestructive testing and evaluation of surface cracks [17; 18; 19; 20; 21]. [PERSON] et al. obtained semi-analytical expression of surface temperature for a model with a semi-infinite crack under laser pulse and lock-in excitation, and verified the validity of the model by lock-in thermography of stainless steel and glass carbon samples [22; 23; 24]. Based on the analytical results under laser pulse excitation, [PERSON] et al. obtained the temperature response under laser scanning excitation by convolution of time, providing a reliable theoretical basis for the optimization of thermographic process parameters of laser scanning thermography (LST) [25]. To reduce the detection time, [PERSON] et al. adopted the line laser scanning thermography method, which could realize the rapid detection of cracks on smooth titanium specimens [26]. In order to improve the identification efficiency and accuracy, [PERSON] et al. proposed a laser multi-mode scanning thermography (SMLT) method combining fast scanning of line laser and fine scanning of point laser, which has a large detection range, detection speed and sensitivity, and can effectively detect the position and shape of cracks on the surface of TBC material [10]. According to the existing research results, the laser thermography method generally determines key thermographic process parameters such as laser scanning speed and power through comparative experiments, and this method has high requirements on the surface flatness of the test piece [27]. However, the surface state of the LR components is complex, e.g., the surface is rough and there are periodic cladding tracks, which poses challenge to the detection of crack defects. It is imperative to optimize the thermal image processing algorithm to improve the efficiency and accuracy of detection [20]. Laser spot thermography is an important way to realize laser thermography. In this paper, in order to improve the detection accuracy and efficiency of laser spot thermography, the optimization of thermographic process parameters and the development of thermal image processing algorithm are studied. Firstly, a simulation model with a vertical surface crack was established based on the finite element method, and the surface temperature response of the model under different heat source parameters was calculated.