# Automatic Assembly Technology of Dense Small Screws for Flat Panel Parts
## 1 Introduction
Neural network models are frequently used for screw assembly quality prediction. [PERSON] et al. determined the key parameters related to screw assembly using an analytic hierarchy process. They also analyzed the pre-tension model of the screw using support vector regression. Accordingly, the prediction of assembly quality and determination of key parameters have been realized [15]. [PERSON] applied machine learning to bolt assembly angle-torque curve analysis as well as detected the classified errors during assembly [14]. [PERSON] et al. collected the muscle and torque signals during screw assembly and processed the collected data using a multi-segment parallel convolution neural network to realize screw assembly quality prediction [16]. Nevertheless, several problems in assembling dense small screws persist and have not yet been resolved by research. (1) The existing visual-based threaded hole positioning methods pose certain risks in the assembly of dense small screws. The main reason is that due to the complex structure of the manipulator, absolute positioning accuracy is difficult to ensure. On the other hand, high-precision manipulators are costly, making them unsuitable for widespread use in ordinary factories. When the manipulator moves over the threaded holes in the longitudinal direction, it is affected by the accumulation of absolute positioning errors. In particular, this is because the distance between two threaded holes is extremely small. Moreover, the target threaded holes may not be in the center view of the camera, leading to positioning errors; consequently, some screws may not be installed. As shown in Figure 1, when locating threaded hole 1 in Step 1, the camera center is closer to threaded hole 2 because of the motion error of the manipulator. Thus, threaded hole 2 is misjudged as the target threaded hole. As the assembly task progresses, in Step 3, a screw is installed in hole 6. However, because threaded hole 1 is outside the camera view, a screw is not threaded into this hole. (2) Angle-torque curves are typically used to determine the screw assembly quality. Teaching methods considerably rely on human experience in artificially setting thresholds. Moreover, the range of torque fluctuations of the screw varies and can easily lead to misjudgment owing to different work scenarios and screw types. In addition, if a method based on neural networks is adopted, a large number of experiments need to be conducted to gather sufficient data to support the training of the neural network [17; 18], which will delay the time to deployment. To complete the assembly of dense small screws on flat panel parts, an automatic assembly system for small screws was developed in this study. The system uses industrial cameras, a laser distance measurer, a torque sensor, and a motor encoder to fabricate a 6R series manipulator. The fabricated equipment has multi-source status sensing capabilities, such as detecting image, distance, torque, and angle, to improve the screw assembly quality. The main contributions of this study are as follows. 1. The position and attitude relationship between the workpiece and basic coordinate systems of the manipulator is obtained using a three-point positioning method with a laser distance measurer. The affine transformation relationship between the pixel and workpiece coordinate system is obtained using a nine-point calibration method, laying a foundation for automatic screw assembly. 2. A visual servo-positioning method has been designed. Firstly, the workpiece is divided into several sub-areas according to the size of the camera's field of view. When the manipulator moves to the center of a sub-area, a photo is taken, and the threaded holes in each sub-area are identified. Secondly, the industrial camera roughly measures the pixel distance between each threaded hole and the center of the area.