# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 1 Introduction  
As an important new laser processing technology, high-power disk laser welding has been increasingly widely used in the manufacturing field. Welding stability monitoring plays an important role in the modern welding process. Therefore, proper sensors and signal processing methods are helpful for detecting the possible presence of weld defects. Various kinds of sensor methods are applied in the field, such as the use of photodiode signals [1; 2; 3], spectroscopy [4; 5], high-speed photography [6; 7; 8], acoustic signals [9; 10], electrical signals [11], X-rays [12], and so on. Among these sensor methods, high-speed photography, which can observe the interaction region between the laser and the workpiece directly, is a kind of non-destructive inspection technique. It has become one of the main development trends for online monitoring of the welding process. [PERSON] et al. [7] designed an active vision system using a high-speed camera to obtain the morphology information of a molten pool during high-power disk laser welding. The casting shadows of the molten pool were analyzed, and four features, including the area, maximal width, and tilt of casting shadow, as well as maximal distance between casting shadow and the keyhole position, were defined. [PERSON] et al. [13] applied a passive vision system using a high-speed camera and an optical filter in real-time measuring of the width and height of weld beads during gasmetal arc welding. The use of optical filter and the existence of radiation generated from the electric arc were sufficient to extract features of the images. The measurement process took less than 3 milliseconds per image, which allowed a transfer rate of more than 300 frames per second. [PERSON] et al. [PERSON] et al. (2017) adopted a high-speed camera and an X-ray transmission imaging system to observe and study the generated spatter and the dynamic molten pool during the high-power laser welding process under different welding parameters. It was found that the ejecting spatter might result in a loss of molten metal and lead to weld defects during laser welding when the focal position was above and on the metal surface. Machine learning algorithms are commonly used in the signal processing method, which are applied in the design, prediction, monitoring and control of the manufacturing process [PERSON] et al. (2017); [PERSON] et al. (2017); [PERSON] et al. (2017). [PERSON] et al. (2017) developed new monitoring algorithms respectively based on cross-validation and a quadratic classifier for ultrasonic metal welding of batteries. [PERSON] and [PERSON] [PERSON] and [PERSON] (2017, 2017) applied a classical statistical process control method in manufacturing process monitoring. Among various machine learning algorithms, support vector machine (SVM) is one method widely used in the manufacturing process. It is based on statistical theory and shows great advantage and good development prospects for resolving small sample, nonlinear, and high dimensional pattern recognition problems [PERSON] et al. (2017); [PERSON] et al. (2017). [PERSON] et al. [PERSON] et al. (2017) applied multisensors to monitor weld defects during high-power disk laser welding. A photodiode sensor was used for monitoring the visible light radiation, an ultraviolet vision sensor was used for plumes and molten pools, and a visual vision sensor was used for monitoring the dynamic behavior of molten pools and keyholes. The SVM classifier has been proven to be efficient in classifying the weld defects in the laser welding process. [PERSON] et al. [PERSON] et al. (2017) studied the use of SVM in the automatic classification of weld defects in radiographic images. The experimental results showed that the SVM classifier was an efficient automatic weld defect classification algorithm. [PERSON] et al. [PERSON] et al. (2017) demonstrated that the SVM model was able to predict the Gas Tungsten Arc Welding process correctly. [PERSON] et al. [PERSON] et al.