Spindle Trajectory Prediction and Workpiece Error Analysis Based On Gradient Boosting Decision Tree

In the context of the Industrial Internet, big data analysis is an important means for digital monitoring, analysis and decision-making in manufacturing factories. Based on the actual large amount of data of Computer Numerically Controlled(CNC) machine tools in a factory collected by digital cloud equipment, the workpiece production, working efficiency and failures of the CNC machine tools are firstly studied in this paper. Aiming at the quality of workpieces produced by specific CNC machine tools, the analysis method of drilling error of machine tools is discussed to judge the quality of workpieces. To better deal with the workpiece quality problems, a method based on Gradient Boosting Decision Tree algorithm to predict the spindle trajectory of CNC machine tools is proposed further. By this method, the existing trajectory data is used to predict the spindle trajectory of the workpieces to be manufactured. The average deviation and prediction accuracy indicate that the method can effectively predict the spindle trajectory of CNC machine tools, which can be used to predict the quality of the workpieces in advance.