Abstract
The development of modern machining and manufacturing industry puts forward higher requirements for the machining accuracy of machine tools. The thermal error of the machine tool spindle directly affects the accuracy of the machined workpiece. To improve the accuracy of thermal error prediction, this paper conducts temperature field analysis for the thermal error of the machine tool spindle and employs the Whale Optimization Algorithm (WOA) to optimize the temperature field parameters, aiming to establish a spindle temperature field model. This approach avoids the problem that traditional measurement methods cannot obtain the temperature of key rotational positions of the spindle and provides a new method for the selection of temperature-sensitive points in the thermal error measurement process. Initially, a spindle Product of Exponentials (POE) error model is constructed to map the five errors of the spindle to three-dimensional vectors in the machine tool space. Subsequently, the Whale Optimization Algorithm (WOA) is used to optimize the physical parameters of the spindle, and the optimal spindle temperature field model is determined. The calculated spindle thermal error data and temperature field model data are input into the OLGWO-SHO-CNN model for training. Finally, a case study is carried out on a machining center, and the trained model is used to perform compensation verification under constant and variable speed conditions, respectively. The experimental results show that under the constant speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 77.2%, 73.1%, and 88.7%, respectively; under the variable speed condition, the compensation rates of the X-axis, Y-axis, and Z-axis are 74.7%, 78.2%, and 88.0%, respectively. The compensation results indicate that the established spindle temperature field model and the OLGWO-SHO-CNN model have good robustness and accuracy.