Research On Precision Machining Process Monitoring System Based On Machine Learning

Intelligent manufacturing is one of the current trends in the development of manufacturing technology.Precision processing equipment is an important part of the manufacturing industry.Its performance and reliability determine the processing efficiency and surface quality of the workpiece.The monitoring of precision processing equipment is to ensure the accuracy of the equipment.An important way to realize smart manufacturing.With the continuous improvement of computer technology and the continuous development of sensor detection technology,data-driven equipment condition monitoring has become one of the most representative intelligent manufacturing modes.Therefore,this subject has developed a condition monitoring system for the precision machining process,and conducted research on data-driven machine learning algorithms to verify the effectiveness of the monitoring system.The specific work is as follows:(1)For the precision machining process,based on the sensing detection technology,the NI-PXIe high-speed acquisition module is used to build a monitoring system hardware platform to establish a precision machining process monitoring system.The system realizes real-time monitoring of the state of the machine tool and the processing process for multiple types of sensors such as acoustic emission,vibration,temperature and power.(2)Complete system software development based on LabVIEW environment.The data acquisition management system subsystem realizes processing signal acquisition,data calling and analysis functions;the database management subsystem completes data storage and other functions to create a precision processing process monitoring database;the decision-making subsystem performs signal analysis and data mining,and completes the machine tool state evaluation function.(3)Research and implement various machine learning algorithms,including principal component analysis and linear discriminant analysis,feature dimensionality reduction algorithms,hierarchical clustering,density clustering,k-means clustering algorithms,learning vector quantization,self-organizing neural networks,Bayesian network and Hidden Markov Model classification algorithms,compare the pros and cons of the above algorithms,and complete the implementation of the system algorithm kernel.(4)The three-axis precision machining machine tool was used as the object to verify the effectiveness of the precision machining process monitoring system and the correctness of data collection,and carried out the grinding wheel life cycle grinding fused silica glass experiment,analyzing the experimental process signals,based on machine learning The algorithm realizes the evaluation of grinding wheel grinding performance degradation.