Robust Locally Weighted Regression for Profile Measurement of Magnesium Alloy Tube in Hot Bending Process

Section flattening often occurs in the hot bending process of magnesium alloy tube with large curvature. In order to control the forming quality of the tube, it is necessary to measure the section profile of the magnesium alloy pipe online. In this paper, the laser vision system is used to measure the profile of magnesium alloy tube. Due to the influence of the environment and the surface quality of the pipe, there are obviously isolated outliers in the profile data, which seriously affects the accuracy and precision of the tube measurement. An outlier identification algorithm based on robust locally weighted regression and Pa(sic)Ta criterion is proposed. This algorithm is used to identify the typically isolated outliers in the measurement process and discuss its identification ability. Meanwhile, it is compared with the moving mean identifier and the Hampel identifier. Subsequently, the ellipse fitting of profile data was carried out, and the fitting ellipse parameters and fitting precision of the curved section were obtained. At the same time, the fitting results were compared before and after the outliers are eliminated. The experiment proves that the outlier identification method based on robust locally weighted regression and Pa(sic)Ta criterion can effectively identify outliers in profile data, especially for spot outliers. This algorithm is a robust, accurate, and efficient outlier identification method, which can effectively improve the laser profile measurement accuracy of the pipe section and has great significance for the quality control of magnesium alloy tube.