No abstract

In this paper, the profile of the bar is detected by laser vision technology. During the detection process, obvious isolated outliers can be observed in the profile data; dimension parameter and profile-fitting accuracy are seriously affected by these outliers. In order to eliminate these outliers and improve the measurement accuracy, this paper uses Hampel identifier and moving mean identifier to identify isolated outliers. At the same time, the profile data is fitted, and the fitting results and fitting accuracy were analyzed and compared between the original data and the renovated data. The experiment proves that the outliers in the data must be identified and processed in the data measurement process. The Hampel identifier has better recognition effect, its algorithm is simple, efficient, and robust, and it can play an important role in the preprocessing of profile data based on structured light.

In this paper, a numerical scheme based on the three-dimensional block-pulse functions is proposed to solve the three-dimensional fractional Poisson type equations with Neumann boundary conditions. The differential operational matrices of fractional order of the three-dimensional block-pulse functions are derived from one-dimensional block-pulse functions, which are used to reduce the original problem to solve a system of linear algebra equations. In addition, the convergence analysis of the proposed method is deeply investigated. Lastly, several numerical examples are presented and the numerical results obtained show that our method is effective and feasible.

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 PaйTa 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 PaйTa 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.