Abstract
Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools
Highlights
Machining accuracy is the most important goal of machine tool producers
To evaluate the prediction ability of the integrated grey system theory (IGST) topology with fixed parameters under different cutting conditions, we adopt the key temperature data from the hard cutting measurement as the input and apply the IGST topology and parameters obtained in the soft cutting case
If we adopt the key temperature data from the soft cutting measurement as the input and apply the IGST topology and the parameters obtained in the hard cutting case, we obtain a prediction result of thermal drift that has a maximal error of 0.0929 mm
Summary
Machining accuracy is the most important goal of machine tool producers. Among the errors occurring in machining, such as the vibration error, thermal error, kinematic error, tool deformation error, and wearing error between moving components, the thermal error, induced by deformation due to the temperature variation, is the primary cause of machining inaccuracy. Despite the issues of thermal error compensation of machine tools, which have been studied for a long time, there still remain some unsolved problems, such as how to establish a robust and precise thermal error model for different kinds of machines. Many past studies considered only the thermal deformation of the spindle since it is the main component that is deformed in machine tools. Only the spindle rotational velocity was considered as the parameter in setting the experimental cutting conditions.[2,10,11] thermal deformation may occur in any component of a machine. To address the aforementioned matters, in this study, we investigate the influence of different machining conditions on the thermal behavior of a machine structure and build a thermal error model.
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