Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

Abstract

To produce a good machine tool, the thermally induced error in the machine during machining plays a crucial role and is an important issue needing to be resolved. The thermal error may account for 70% of the total error. There are three main approaches to solving the thermal error problem: preventing heat flows from hot components, designing a thermally stable structure for the machine, and compensating the thermal error using thermal error models. The first two approaches can be carried out in the primary design stage of machine tools, and they have been used in the manufacture of commercial products. The third approach, the strategy of thermal error compensation, is the most effective and popular approach. However, there are still many unsolved problems. Among these problems, the cutting conditions have a significant influence on the modeling precision of the thermal error. In this study, we develop an integral model based on the integrated grey system theory (IGST) in conjunction with a genetic-algorithm (GA)-optimized back-propagation neural network (BPNN) to investigate the influence of cutting conditions on a machine tool's thermal error. The model is chosen on account of its high ability in dealing with a small amount of training data. Results show that a single thermal error modeling formula cannot make accurate predictions for different cutting conditions. Suitable adjustment of the modeling parameters or the use of a multiple modeling scheme is needed.

A Novel Two-Mode Integral Approach for Thermal Error Modeling in CNC Milling-Turning Machining Center

“…After that we need to build an accurate TEM with suitable machine learning schemes to predict the deformations of the milling as well as the turning spindle. Recently, SVR and deep-learning neural network (DNN) schemes, known as good methods for dealing with highly non-linear mapping problems, were frequently used in the thermal error modeling problems of CNC machine tools [18,19]. Therefore, to build a proper TEM of our complex milling-turning machine tool, we adopt SVR, DNN, and an excellent random forest (RF) methods to predict thermal deformations.…”

“…How to successfully suppress the thermal deformation is an important work. Against this issue, various methods including the avoidance of thermal displacement, control of heat transfer, and compensation of thermal error have been developed [2][3][4]. In general, the aforementioned first two methods have already been fully considered in the design stage for modern machine tools development.…”

“…For example, Wang et al [16] used the rough set theorem to reduce the measured temperature data and found the knowledge kernel for help establishing a precise TEM. Recently, accompanied by prosperous developing of deep learning neural network schemes, scholars begun to use the convolution neural network scheme as the mapping function of TEM [17,18,19], whose prediction ability is regarded as better than the other aforementioned machine learning schemes.…”