Structural aging and environmental changes associated with long-term operation can substantially modify the thermal behavior of machine tools, diminishing the accuracy of existing thermal compensation models. Traditional neural network approaches typically necessitate time-consuming and inefficient retraining from scratch for practical adaptation. To address this limitation, this study proposes a parameter-based transfer learning technique to enhance model adaptability under evolving machine tool operating conditions. The method establishes a composite fine-tuning architecture by adding hidden layers and selectively freezing neural network parameters, enabling the rapid adaptation of the pretrained model to new thermal characteristics using limited data. A full-factorial experimental design identified the optimal configuration, comprising (i) structural expansion via an LSTM layer inserted after the hidden layers; (ii) a strategy freezing parameters in all layers; and (iii) training under the selected optimal condition (C9), which reflects machine tool characteristics and environmental temperature variations. The baseline model achieved an RMSE of 3.88 µm. Traditional retraining using the complete dataset and retraining only on C9 yielded RMSE values of 3.21 and 3.84 µm, respectively. In contrast, the optimized transfer learning model trained on C9 achieved an RMSE of 3.47 µm. Experimental results demonstrate that the proposed strategy converges with limited data, reducing the number of datasets from 18 to nine and significantly shortening training time from 18 h 20 min to 30 s. This approach offers an effective solution for sustainable model maintenance and expedited industrial deployment.
Chuang et al. (Mon,) studied this question.