Thermal error modeling and prediction analysis based on OM algorithm for machine tool’s spindle

Abstract

The manufacturing precisions of machine tools are seriously affected by high-speed spindle thermal error, especially the axial thermal deformation. A thermal error prediction model is an effective and economical approach to enhance the accuracy of machine tools, which is based on various artificial intelligence algorithms. This paper puts forward a novel optimal effective composite model (OM) for spindle thermal error prediction, which integrates the advantages of both gray model (GM(1,n)) and least squares support vector machine (LS-SVM) in terms of the experiment sample data. In the first place, the GM(1,n) and the LS-SVM are borrowed to establish the spindle thermal error prediction model, respectively. Then, the OM model is built by optimizing and adjusting the weighting coefficient of GM(1,n) and LS-SVM model, which is predicted by practical thermal error sample data. Finally, the prediction accuracy of the OM model is better than GM(1,n) model and LS-SVM by comparing the above models. After compensation, the maximum spindle thermal error, dropping from 16.4 to 3.5 μm, is significantly reduced with a droop rate of 78.7%. Therefore, the results show that, comparing with traditional GM(1,n) and LS-SVM method, the OM presented in this paper is more accurate and robust for thermal error prediction and compensation under complex machining conditions, which has preliminarily industrial application prospect.