Design of Surface Quality Monitoring System for CNC Machine Tool Parts based on Edge Computing

Abstract

In response to the problems of surface quality monitoring methods during the process of processing parts in the process of processing parts, there are problems such as low accuracy and poor real -time real -time. This article designs a marginal computing of the surface quality monitoring system of CNC machine tool processing parts. First of all, the system calculates the equipment through the edge computing equipment deployed near the CNC machine tool to collect the vibration signal data during the processing process. Then use the LSTM deep learning algorithm to process and train data. Finally, the training completed high -precision prediction model is introduced into the edge computing device to achieve real -time monitoring of the surface quality of the parts. The experimental results show that the system of the system's surface roughness prediction is 0.04, and the surface quality of the parts during the processing process can be monitored in real time, which significantly improves the intelligent level and quality control capacity of the processing process.