Research on intelligent monitoring and optimization model of machining process based on deep learning

With the development of the manufacturing industry in the direction of intelligence, the intelligent monitoring and optimization of machining processes have become an important research topic to improve production efficiency and machining quality. Traditional monitoring and optimization methods rely on experience and simple signal analysis, which are difficult to meet the demand for high accuracy in complex machining environments. For this reason, this paper proposes a deep learning-based intelligent monitoring and optimization model for machining processes. The model uses sensor data to collect key parameters in the machining process, carries out feature extraction, fault detection and machining quality prediction through deep neural networks, and optimizes the machining parameters by combining deep reinforcement learning to achieve efficient and adaptive machining control. This paper first analyzes the key monitoring variables in the machining process and the applicability of the deep learning method, then designs the data acquisition and preprocessing process, and constructs an intelligent optimization model based on deep learning, and finally verifies the effectiveness of the model through experiments. The results show that the method can accurately identify abnormalities in the machining process, improve the quality and stability of machining, and has important application value for the monitoring and optimization of machining in the field of intelligent manufacturing.