Normalized Variational Auto-Encoder With the Adaptive Activation Function for Tool Setting in Ultraprecision Turning

To ensure the machining quality of micro/nano scale structural units for meter scale workpieces, relay turning with multiple single-point diamond tools has been broadly required. However, the existing tool setting methods have the problems of long tool setting time and low tool setting accuracy. To address the above issues, a novel normalized variational auto-encoder model with an adaptive activation function (NVAE-AAF) is proposed in this article. The batch normalization and the adaptive activation function are introduced into the variational auto-encoder model to learn robust features of force signals at the tool idle move state. Then, the reconstruction error threshold is constructed according to the kernel density estimation method to realize the nanoscale tool setting. In the ultraprecision tool setting experiments based on piezoelectric ceramic force sensing, the reconstruction error of the force signals at the tool idle move state is less than 0.07%, and the contact detection accuracy reached 92%. Compared to the traditional trial cutting for tool setting method, the proposed method significantly improves tool setting accuracy by 75%–85%, reaching a level of 75 nm.