Real-time milling force monitoring based on a parallel deep learning model with dual-channel vibration fusion

Abstract

Milling force is one of the most important aspects of milling. Its dynamic excitation effect significantly impacts both product quality and machining productivity. Nevertheless, the force amplitude changes dramatically when the tool and the workpiece begin to contact or separate. Most current research does not consider this phenomenon. This article presents a parallel integration deep learning approach to address the issue. First, this study analyzes the relationship between milling force and vibration signals and sets the dual-channel vibration signals in the same direction as the model's inputs. Then this study proposed an encoder-decoder network to realize force monitoring. Considering that the acquired vibration signal contains much noise and needs to be preprocessed, the encoder comprises long-short term memory(LSTM) networks and a fully connected (FC) network to realize adaptive filtering and feature extraction. Multiple-layer FC network forms the decoder part to reconstruct the milling force signal because of the nonlinear relationship between the vibration and force signals. Thirdly is to obtain the parallel monitoring model. The first monitoring model is obtained through the training procedure. The results of the first model are subtracted from the measured cutting force signal to get the residual part. Then, the residual part is set as the output while training the residual monitoring model. Finally, the force monitoring model is derived using the parallel integration method. The experimental results demonstrate that this study's monitoring model can provide real-time, high-precision, and reliable milling force monitoring under various cutting conditions.