Papers by Author: J.H. Shen

Abstract: This paper presents a partial least squares neural network modeling method for CNC machine tool thermal errors. This method uses the neural network learning rule to obtain the PLS parameters instead of the traditional linear method in partial least squares regression so as to overcome the multicollinearity and nonlinearity problem in thermal error modeling. The basic principle and architecture of PLSNN is described and the new method is applied on the thermal error modeling for a CNC turning center. After model validation with two groups of new testing data and performance comparison with other five different modeling methods, PLSNN performs better than the others with better robustness.

Abstract: The volumetric positioning precision of CNC machine tools is the key factor to get high machining precision, so the analysis, measurement and compensation of the volumetric error is becoming more and more important. In this paper, the modeling results of 3-axes CNC machine tools with four different configurations are given based on rigid body theory and homogeneous coordination transformation matrices. An improved sequential step diagonal measurement method is proposed and analyzed because the current laser measurement methods are complex and time cost. At the final section of the paper, the measurement data was applied into the error compensation and the sequential step diagonal measurement method was validated efficient and convenient.