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.

CNC machine tools have high requirements for precision and smoothness of motion. As the power source, the performance of the servo motor has a significant impact on the movement of the machine tool. Harmonic is a common phenomenon in motors, which brings adverse effects on the performance of the motor, even the whole machine and the machined parts. Therefore, it’s of great significance to study the source, characteristics, and influencing factors of the harmonics. In this paper, the time domain modeling, simulation, and analysis are carried out for the harmonic phenomenon of the surface-mounted 3-phase AC permanent magnet synchronous motor (PMSM). Firstly, a dynamic model of the servo motor integrated with its control and driving system is established under ideal conditions, and the simulation of the motor running state is realized in the time domain. On this basis, the modeling and simulation are carried out for the inverter dead-zone, cogging torque, rotor mixed-eccentricity, and 3-phase asymmetry, respectively, and the frequency components of the harmonics caused by these factors are obtained. Finally, the influence of load and motor rotational velocity on 3-phase current and torque harmonics is analyzed, and the interactions between the inverter and the motor are investigated. The results show that the inverter and the motor are coupled to each other as an integrated system, and they have impacts on each other. The established model is verified by experiments.