Fast reconstruction of milling temperature field based on CNN-GRU machine learning models

With the development of intelligent manufacturing technology, robots have become more widespread in the field of milling processing. When milling difficult-to-machine alloy materials, the localized high temperature and large temperature gradient at the front face of the tool lead to shortened tool life and poor machining quality. The existing temperature field reconstruction methods have many assumptions, large arithmetic volume and long solution time. In this paper, an inverse heat conduction problem solution model based on Gated Convolutional Recurrent Neural Network (CNN-GRU) is proposed for reconstructing the temperature field of the tool during milling. In order to ensure the speed and accuracy of the reconstruction, we propose to utilize the inverse heat conduction problem solution model constructed by knowledge distillation (KD) and compression acceleration, which achieves a significant reduction of the training time with a small loss of optimality and ensures the accuracy and efficiency of the prediction model. With different levels of random noise added to the model input data, CNN-GRU + KD is noise-resistant and still shows good robustness and stability under noisy data. The temperature field reconstruction of the milling tool is carried out for three different working conditions, and the curve fitting excellence under the three conditions is 0.97 at the highest, and the root mean square error is 1.43°C at the minimum, respectively, and the experimental results show that the model is feasible and effective in carrying out the temperature field reconstruction of the milling tool and is of great significance in improving the accuracy of the milling machining robot.