# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 1 Introduction  
Because of its high production quality, laser welding has become one of the most important welding techniques in the modern manufacturing industry [1, 2]. However, compared to traditional welding technologies, the energy transmission during laser welding is complex and unstable, which makes it a formidable challenge to monitor the laser welding process. Therefore, developing effective and accurate monitoring methods is of great importance in guaranteeing process stability and the product quality. Video cameras offer the unique capability of collecting high-density spatial data from a distant scene of interest. They can be employed as remote monitoring or inspection sensors for structures. [PERSON] et al. [3] have done a lot of research work in the field to advance the methodology towards the eventual goal of using a camera as a sensor for the detection and assessment of structures. They propose some novel vision-based methods using a deep architecture of convolutional neural networks for detecting loosened bolts [3] and concrete cracks [4]. Geometric features varying three dimensionally are extracted from two-dimensional images taken by the camera [3, 4]. They have demonstrated motion magnification for extracting displacements from the high-speed video [4], and demonstrated the algorithm's capability to qualitatively identify the operational deflection shapes of a cantilever beam and a pipe cross section from a video [5, 6]. Furthermore, new damage detection methodologies have been proposed by integrating a nonlinear recursive filter [6] and using a density peaks-based fast clustering algorithm; damage-sensitive features are extracted from the measured data through a non-contact computer vision system [7]. Computer-aided analysis techniques have been widely applied to monitor manufacturing processes [8; 9]. Especially since their recent rapid development, machine learning and pattern recognition methods have attracted considerable attention. [PERSON] et al. [10] developed an efficient quality monitoring system for small scale resistance spot welding based on dynamic resistance. A back propagation neural network was used to estimate the weld quality, and showed a better performance than regression analysis. [PERSON] et al. [11] investigated the main effects of process parameters on laser welding process quality. An artificial neural network model was built to forecast the features of the welding bead. Experiment results showed that the neural network was able to predict with significant accuracy. Support vector machines (SVM) are a practical method for classification and regression. It is motivated by the statistical learning theory developed by [PERSON] et al. [12; 13], and has been successfully applied to a number of applications in the welding industry. [PERSON] et al. [14] explored the relationship between the welding process and welded quality. A multiple sensor fusion system was built to obtain the photodiode and visible light information, and SVM was used to classify three types of weld quality. Experiment results showed that the estimation on welding status was accurate. [PERSON] et al. [15] developed an automatic recognition system for welding seam types; an SVM-based modeling method was used to achieve welding seam type recognition accurately. [PERSON] and [PERSON] [16] used SVM to automatically classify four types of weld defects in radiographic images. Nevertheless, the performance of SVM is highly affected by the features used. Lack of precise a priori knowledge, and selecting features blindly may lead to unsatisfactory classification accuracy. In this work, three welding experiments were conducted at different welding speeds using high-power disk laser. The images of laser-induced metal vapor during welding were captured by a high-speed camera. Fifteen features of the metal vapor plume and spatters were extracted from each image. A feature selection method was employed to identify the optimal feature subset, and an SVM classifier was built to evaluate the welding quality automatically.