An analytical approach for crucial geometric errors identification of multi-axis machine tool based on global sensitivity analysis

Abstract

Geometric errors directly affect the tool tip position, reduce machining accuracy, and are one of the most important errors of multi-axis machining tool. However, the geometric errors are intercoupling, and the measured values at different points vary and are stochastic. The identification of the most crucial geometric errors and the determination of a method to control them is a key problem to improve the machining accuracy of machine tool. To achieve this goal, a new analytical method, to identify crucial geometric errors for a multi-axis machine tool is proposed here based on multibody system (MBS) theory and global sensitivity analysis. The volumetric error modeling of multi-axis machine tool has been given by MBS theory, which describes the topological structure of multibody system simply and conveniently in a matrix. The stochastic characteristic of geometric errors is taken into consideration and Sobol global sensitivity analysis method is introduced to identify crucial geometric errors of machine tool. A vertical machining center is selected as an illustration example. The analysis results reveal that the analytical method presented in this paper can identify the crucial geometric errors and are helpful to improve the machining accuracy of multi-axis machine tool.

Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation

“…The corresponding relationships between the basic geometric errors and accuracy parameters of the components (Cheng et al 2014) are listed in Table 17.…”

“…In recent years, much research work has been done on modeling of multi-axis machine tools to find out the resultant error of individual components in relation to tool and work-piece point deviation (Bohez 2002a). And, the modeling methods of the geometric errors from different perspectives have experienced several developing phases (Cheng et al 2014). To describe the error of cutter location and tool orientation between the two kinematic chains, the error model is normally established using HTM (Liu et al 2011;Eman et al 1987;Lei and Hsu 2003), D-H (Jha and Kumar 2003), MD-H (Lin and Tzeng 2008), or Multi-body system (MBS) (Zhu et al 2012;Zhang et al 2003) theory.…”

“…Chen et al (2013) studied the volumetric error modeling and its sensitivity analysis of a five-axis ultra-precision machine tool, and based on the local sensitivity analysis results, the key error components have small reduction, which decreases the difficulty to control the accuracy of the machine tool. Cheng et al (2014) took the stochastic characteristic of geometric errors into consideration and used the Sobol' global sensitivity analysis method to identify crucial geometric errors of machine tool, which is helpful to improve the machining accuracy of multi-axis machine tool.…”

A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

“…In recent years, many studies have focused on modeling multi-axis machine tools to determine the resultant error of individual components in relation to the set-point deviation of the tool and the workpiece. Furthermore, the various methods for modeling the geometric errors from different perspectives have experienced a gradual development [7]. To describe the error of the cutter location and the tool orientation between the two kinematic chains, the error model is normally established using homogeneous transformation sciENcE aNd tEchNology matrices (HTM) [10,18,20], denavit-hartenberg (D-H) method [16], modified denavit-hartenberg (MD-H) method [19], or multi-body system (MBS) theory [31,32].…”

“…Based on the results of the local sensitivity analysis, they were able to slightly reduce the key error components, which made it easier to control the accuracy of the machine tool [6]. Cheng et al considered the stochastic characteristic of the geometric errors and employed Sobol's global sensitivity analysis method to identify the crucial geometric errors of a machine tool, which is helpful for improving the machining accuracy of multi-axis machine tools [7]. De-Lataliade et al developed a method based on Monte Carlo simulations (MCS) for estimating the reliability sensitivity [8].…”