Thermal Error Modeling of a Machine Tool Using Data Mining Scheme

Abstract

In this paper the knowledge discovery technique is used to build an effective and transparent mathematic thermal error model for machine tools. Our proposed thermal error modeling methodology (called KRL) integrates the schemes of K-means theory (KM), rough-set theory (RS), and linear regression model (LR). First, to explore the machine tool's thermal behavior, an integrated system is designed to simultaneously measure the temperature ascents at selected characteristic points and the thermal deformations at spindle nose under suitable real machining conditions. Second, the obtained data are classified by the KM method, further reduced by the RS scheme, and a linear thermal error model is established by the LR technique. To evaluate the performance of our proposed model, an adaptive neural fuzzy inference system (ANFIS) thermal error model is introduced for comparison. Finally, a verification experiment is carried out and results reveal that the proposed KRL model is effective in predicting thermal behavior in machine tools. Our proposed KRL model is transparent, easily understood by users, and can be easily programmed or modified for different machining conditions.