Quantitative Evaluation of Servo Dynamic Characteristic Matching for Multi-Axis Cnc Machine Tools

Abstract

As CNC machine tools continue to develop in the direction of high speed and high precision, the impact of dynamic performance on machining accuracy is becoming increasingly significant. Dynamic performance optimization has become an important means to improve machine tool processing efficiency and ensure machine tool processing accuracy. Servo dynamic characteristic matching is one of the key factors affecting the dynamic performance of multi-axis CNC machine tools. The optimization of servo dynamic characteristic matching has the advantages of low cost, strong operability, and significant improvement in processing accuracy, and has become a hot research topic in the dynamic characteristics of CNC machine tools. The evaluation of the state of servo dynamic characteristic matching is the research basis for optimizing servo dynamic characteristic matching and related research is of great significance for optimizing machine tool dynamic performance. Currently, five-axis machine tools commonly use the processing of S specimens to detect and evaluate dynamic accuracy. However, further promotion and application of the method is limited by the lack of quantitative mathematical description of the state of servo dynamic characteristic matching, the interrelated factors which have complex mapping relationship with the S test specimen contour error and few studies on the identification of dynamic accuracy influencing factors. Therefore, based on the processing and contour error of the S specimen, a quantitative evaluation method for the state of servo dynamic characteristic matching was proposed. The method applies the K-means clustering method, which is one of the data mining algorithms, to the sensitivity analysis of the contour error of the S specimen to the servo dynamic characteristic matching. Based on multiple linear regression, an evaluation model is established to achieve a preliminary evaluation of the state of servo dynamic characteristic matching. Firstly, based on the command trajectory and time-domain tracking error model, two parameters were defined in this study to quantitatively describe the servo dynamic characteristic matching. The tool axis trajectory surface was used as a medium to analyze the relationship between the servo dynamic characteristic matching and the S specimen contour error. The sensitivity of the contour error of each region of the S specimen to the servo dynamic characteristic matching states of the five-axis machine tool was analyzed using semi-supervised K-means clustering method. Finally, considering machining process and the dynamic accuracy detection standard of the S specimen, an evaluation model was established based on multiple linear regression. The effectiveness of this evaluation method was verified through relevant experiments.

Keywords: Servo dynamic characteristic matching, S specimen, multi-axis machine tools, mathematical description, K-means clustering