## 1 Introduction  
Under the trend of Industry 4.0, the concept of unmanned factories has dramatically emerged. All the governments over the world are also promoting smart manufacturing policies. It is expected that by developing unmanned smart factories to cope with the current shortage of labor, many manufacturers have introduced production lines combined with the robotic arm. Especially, in the metal forming industry, manual work in the current situation still plays an important role [1]. Hence, to improve production efficiency and reduce labor costs, as well as reduce the danger of human loss when working with the high-temperature workpiece in the hot-stamping process [2], it is required that the objects are first arranged and placed in a position, and then the robot arm is used to pick up and unload the materials to achieve the loading and unloading of the objects. The pre-work then is more troublesome. In order to fully achieve automation of new-generation smart factories, the combination of robotic arms and visual images has gradually become the current development trend. [PERSON] et al. [3] illustrate an idea to convert the current standard cold forming process of curved panels in the shipbuilding industry to computerized process control and quality examination using a conventional press, crane set up, and point cloud model based on a 3D vision system to monitor the production process successively. As a suggestion for our study to utilize three-dimensional images in the stamping industry, we have processed the point cloud to estimate the six-axis degree of freedom position and feature point information of hot-stamping workpieces. The latest modern Microsoft Azure Kinect camera [4], which is an application of the Time of Flight (ToF) principle [5],is the vision device used for this application to establish a 3D point cloud model in the scene. Later, the point cloud is leveraged to process the 3D point cloud data through a point-to-feature matching algorithm [6] to identify the stamping workpiece and its 6-DoF (6 degrees-of-freedom) position. The stamping workpieces in this study are placed on a conveyor to perform dynamic pick-and-place. [PERSON] et al. [7] have a similar method of using a Kinect camera and a laser scanner to construct the point cloud and identify the position of the fruits, which were later used for harvesting. Compared to their research, a system with two depth cameras calibrated by stereo calibration in our study will generate both a target model point cloud and a reference model point cloud. Hence, the demand for a lower processing time as well as accuracy can be met. Moreover, setting up two cameras with two different viewing angles also allows the point cloud to fully model the object with just one scan. After the 6-DoF position of the workpieces is figured out, they will be transferred by the conveyor and the robot arm will pick them into the stamping die. [PERSON] et al. [8] stated that in a hot-stamping production line of high-strength steel, in order to evade oxidation and plasticity reduction when the temperature falls, the sheet metal has to be shifted from the furnace to the stamping die as quickly as possible. Therefore, they have proposed a method of synchronizing the motion of the feeding system. A description related to the velocity of the conveyor and the moving speed of the robot is executed for the purpose of dynamic transmission as our main goal of this study. After being placed into the machine, the system will trigger a signal for stamping to move and complete stamping. Finally, the system achieves the dynamic workpiece feeding process.