Study On Intelligent Tool Wear Monitoring And Cutting Force Prediction

Tool wear and tool breakage are inevitably phenomenon occurring in the process of metal cutting, the transformation of tool-state directly cause product quality declining, production cost increasing, and gradually affect the ability of market competition of products. Therefore it is necessary to do researches on the tool wear monitoring technique in NC machining process. This thesis deals with the following researchers:In-depth studied the different of the comparative test method and orthogonal test method and researched the different methods impact of data detection and forecast. By comparing the test methods to obtain the network modeling, fuzzy cluster, etc. required to complete data pattern recognition; by orthogonal test method was successfully from a limited experiment, with a full range, continuous, predictable turning information.We analysis the signal characteristics with the variation of different factors, flexible and effective feature extraction based feature definition, for tool wear monitoring. Done with cutting force signal, vibration signal time-domain analysis, frequency domain analysis and wavelet analysis, based on the dynamic characteristics of multi-processing conditions for monitoring tool wear under the new method to improve the monitoring accuracy of the feature are conducive to the establishment of knives the characteristic value table.Analyzing BP neural networks, fuzzy cluster and other pattern recognition applied to the characteristics of tool wear monitoring. We respectively use them to do the tool wear state recognition.Based on the monitoring of the theory, this thesis creative application of Six Sigma principles-based prediction of cutting forces will be applied to milling and turning. A large number of validation test results show that the method can predict the cutting of the cutting force. It is conducive to the promotion of industrial research.This thesis in the experimental design, signal analysis, monitoring strategy, feature extraction and selection, pattern recognition and trend forecasting carried out an active exploration. It is conducive to resolve the technical and practical tool for monitoring of a number of issues arise. And for the monitoring system, a practical study and explore new ideas enriched and developed the tool wear monitoring.