## 1 Introduction  
Selective laser sintering (SLS) is a powder bed-based additive manufacturing where parts are made directly from three-dimensional CAD data layer-by-layer from the fusion of powder materials [1]. SLS can be used to process a variety of metal materials, and the parts usually have good dimensional accuracy and surface quality [2]. SLS is one of the major additive manufacturing processes that have been widely adopted by industry and investigated by researchers and practitioners. However, physical, chemical, mechanical, and material metallurgical phenomena are extremely complex in the SLS process. It is difficult to develop accurate knowledge about the true state of equipment and the quality of parts during the process. Most often, the stability of the process parameters and the repeatability of the technique cannot be ensured. Therefore, the online prediction of quality issues during the SLS process is extremely challenging, if not impossible. Typically, defects are detected by destructive or non-destructive methods in the as-built parts after the building process is completed, while the causes of the defects are difficult to trace. In the building process, the temperature field of the working area can quantitatively characterize the real-time states of the metal powder coating and melting pool in time and space, and can indirectly reflect the actual values of the process parameters such as laser power density, scanning track, and scanning speed. According to the characteristics of SLS technology and equipment, an infrared thermal imager is used to collect the temperature field images at any time in the process. By analyzing the temperature field images, the incidence relationship between the temperature field and the states of powder coating and melting pool is established to realize the monitoring and quality control of the SLS process [3; 4]. The [PERSON] team proposed using an infrared thermal imager to acquire image data in the additive manufacturing process, and analyzed the temperature change with time, the state of laser-powder interaction area, and the change of sputtering radiation, and also emphasized the importance of analyzing global temperature data in both time and space dimensions to identify unstable factors in the process [5; 6]. The [PERSON] team built a temperature measurement and online analysis system for the quality control of selective laser melting. By analyzing the image data of the melting pool, abnormal conditions such as deformation, local overheating, and porosity due to thermal pressure were discovered in advance [7; 8; 9]. The [PERSON] team analyzed the sensitivity of the irradiation level to process parameter fluctuations and porous defects by experimental means, and established a simulation model of the additive manufacturing process based on ANSYS. Macro and micro temperature image features were analyzed by simulation experiments, and the definitions of key features such as the maximum temperature value, thermal diffusivity, and sputtering activity were proposed [10; 11; 12; 13]. Many researchers have focused on the features of the melting pool. [PERSON] discussed the effects of the geometric state of parts, laser energy distribution, surface oxidation, and other factors on the temperature of the melting pool [2]. [PERSON] analyzed the effects of the process parameters on laser attenuation and the heating of powder particles by recording the temperature images of the melting pool [14]. [PERSON] collected the images of the melting pool by experimental means, and tried to analyze the mutual influence among various factors [15]. Others explored the relationship among process parameters, temperature, and product quality from the aspects of the influence of impurities on product quality and typical abnormal conditions of the process, and subsequently constructed a simulating model of the additive manufacturing process based on the finite element method [16; 17; 18].