A hyperparameter optimization-assisted deep learning method towards thermal error modeling of spindles

Spindle thermal errors significantly influence the machining accuracy of machine tools, necessitating precise modeling. While deep learning methods are commonly used for this purpose, their generalization ability and performance largely depend on design of the network structure and the selection of hyperparameters. To address these challenges, this study proposes a neural network model that integrates Bayesian optimization (BO) with dilated convolution neural network (DCNN). Dilated convolutions enhance traditional CNN models by using a dilation rate, which allows the convolutional kernel to cover a larger receptive field without increasing parameter count or computational cost. To prevent local optima during hyperparameter tuning, a Bayesian algorithm based on Gaussian processes (GP) is utilized, which optimizes 9 critical hyperparameters in the DCNN. Experimental results demonstrate that the proposed model achieves over 95 % accuracy in predicting radial thermal errors for both heating and cooling states in the X and Y directions.