Abstract
Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle.
Highlights
The machining errors of machine tools mainly include geometrical errors, thermal errors, errors caused by cutting-force, fixture-dependent errors, etc. [1,2,3,4]
If the accuracy requirement is not very difficult, one can choose just the temperature points nearing the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle
Though the thermal error model for estimating the thermal error of the spindle from its feature temperature points is a regression problem, random forests is a better method than Gaussian process regression
Summary
The machining errors of machine tools mainly include geometrical errors, thermal errors, errors caused by cutting-force, fixture-dependent errors, etc. [1,2,3,4]. The machining errors of machine tools mainly include geometrical errors, thermal errors, errors caused by cutting-force, fixture-dependent errors, etc. The heat sources in the machining process of machine tools include two categories: internal heat sources, and external heat sources [2,4]. Internal heat sources include the heat produced by cutting, the heat induced by the friction of bearings, spindle, gearbox, and motion guides; the heat generated in the motor; and the heating or cooling effects produced by the cooling system. Being a key component of the machine tool, the spindle will generate a lot of heat during the machining process and thereby result in a thermal deformation/error of itself. The main heat sources of the spindle are the friction of bearings and the heat generated by the motor
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