# Automatic Assembly Technology of Dense Small Screws for Flat Panel Parts
## 1 Introduction  
The performance of complex electronic devices such as radar is affected by many factors, such as the material ratio of components and assembly accuracy [1], of which assembly accuracy is a very important aspect. Radar requires many screws to fix high-precision structural parts. The screws are small, densely distributed, and of various types [2]. As a result, the manual assembly of screws is inefficient. For automatic assembly equipment, a small screw assembly requires high positioning accuracy. Otherwise, screw assembly failure can easily result in damaged surface parts and missing screws, negatively affecting product quality. In view of the foregoing, the automatic assembly of small screws is not currently popular [3]. Accordingly, the investigation of automatic assembly equipment for dense small screws is necessary. Currently, the key steps in automatic screw assembly are screw hole positioning and screw tightening [4]. Most factories rely only on drawings to obtain the position information of threaded holes and control mechanisms to identify the corresponding positions for screw assembly. For example, [PERSON] et al. [5] designed a six-revolute (6R) manipulator screw operation system that relied on the self-positioning capability of a manipulator to locate a threaded hole. The manipulator is controlled by an additional force sensor such that it is flexible in the workpiece plane and can be used to adjust the screw position. However, owing to the inherent structural errors of the manipulator, its absolute positioning accuracy may be as high as several millimeters [6]. Moreover, the actual workpiece coordinate system typically has errors; hence, it is not an ideal workpiece coordinate system. Therefore, the sole reliance on the self-alignment of the manipulator does not ensure that the screw is correctly placed in the threaded hole, causing the screw to destroy the surface of the assembled part. To improve the positioning accuracy of threaded holes, visual measurement technology has been introduced to modify their position information. After moving the industrial camera over the threaded holes according to the drawing information, a more accurate position can be obtained by visually measuring the screw assembly [7; 8]. [PERSON] et al. developed a threaded hole recognition and positioning technology based on machine vision technology. It located the holes based on edge detection and Hough transformation as well as measured the coordinates of thread holes for guiding the manipulator to complete the assembly [9]. [PERSON] et al. designed an automatic locking system for a manipulator with four degrees of freedom. The position and attitude errors of the screws were monitored in real time based on fuzzy control and visual servo feedback strategy [10]. [PERSON] et al. used machine vision for guiding SCARA robots to locate threaded holes such that these machines could be applied to the automated production line of microscrews [11]. In actual assembly processes, the screw assembly may fail or be disqualified because of the measurement and motion errors of the manipulator; accordingly, determining the assembly quality of screws is critical. Currently, teaching methods are commonly used in the industry. This method assumes that each screw has a unique angle-torque curve during assembly and that the fault is typically a deviation from the correct curve [12]. This method compares the real-time torque curve with the characteristic torque curve during assembly. If the screw assembly characteristic curve follows the set track and stops within the final threshold area, then the screw assembly is considered successful [13]. Although this method is easy to implement and typically reliable, it has insufficient flexibility and setting the corresponding characteristic torque is time consuming. Moreover, because only the threshold for important nodes is set, the trend of curve changes between two nodes is ignored, easily leading to misjudgment [14].