Research On Visual Diagnosis Method Of CNC Tool Based On Manifold Learning

With the rapid development of the manufacture,industrial production technology is also advancing with the times.As the main execution unit of cutting manufacturing,tool conditions directly affect the accuracy,efficiency and safety of the machining process.On-machine monitoring of tool conditions is one of the key technologies for manufacturing automation,automation and networking.Based on the traditional tool monitoring,this paper designs a tool diagnosis platform based on embedded and image processing,and combines manifold learning methods to identify tool working conditions and ensure the safe operation of the processing system.The main content of the paper includes:According to the analysis of tool wear mechanism,it is determined that the tool flank face is the main research object,the monitoring equipment is selected according to the platform requirements,the diagnosis platform plan is determined,the image preprocessing process and the overall hardware circuit design are improved.The embedded circuit,the image acquisition module,the CNC module and the network communication module are used to design the hardware circuit.The data communication between the visual diagnosis platform and the numerical control system is realized by the PROFIBUS bus protocol,and the visual diagnosis platform is built and the hardware test is completed.Aiming at the extraction of tool wear characteristic values,this paper combines OpenCV open source vision library,uses region growing algorithm to segment the tool wear area,minimizes the circumscribed rectangle to extract the tool wear shape feature value,and gray level co-occurrence matrix extracts the texture feature value.For the identification of tool working conditions,a tool learning method based on manifold regularization is proposed.Manifold regularization support vector machine classifier is built.The wear characteristic value and cutting parameters are used as sample to classifiers.The test data is verified.The working condition recognition rate is 95%.The Qt framework is used to build a remote monitoring platform,and the platform user login registration,parameter setting,phased image display,and data storage functions are developed.Based on the TCP/IP protocol,the data communication between the visual inspection platform and the monitoring platform is realized.Finally,the comprehensive experimental test of the tool inspection platform is carried out,the tool image is successfully collected and the wear characteristic value is extracted,and the data processing transmission display is correct,and the expected effect is achieved.