The Health Evaluation Technology Of The CNC Tool

During the machining process of the Cutting of the flexible material CNC tool,the Cutting tool may be subject to wear due to physical friction,diffusion wear,thermal fracturing,plastic deformation,and grain shedding.The wear of the Cutting will not only reduce the processing quality of the flexible part,but also affect the surface roughness and dimensional accuracy of the flexible part.At the same time,it will also seriously affect the stability and processing efficiency of the flexible material CNC cutting machine.The thesis aims to establish the state health prediction of the key parts of CNC tool for flexible materials.The research includes the analysis of the wear characteristics of the Cuttings of flexible materials and the feature extraction methods.The data characteristics of Cuttings of the NC cutters for flexible materials are studied to study the health of Cuttings.Predicting the Markov method,put forward the gray-hidden Markov model to predict the health status of the Cutting,and designed the health evaluation technology of the Cutting of the flexible material CNC tool,which promotes the development and application of the intelligent maintenance technology of the CNC cutting machine for flexible materials.Important theoretical research value and practical application significance.The paper discussed the domestic and foreign research progresses in the related research fields of processing data feature extraction methods and processing equipment PHM prediction methods.It began with the analysis of the Cutting wear process,analyzed the health conditions of the Cutting,and used the time-frequency domain method and wavelet method.Extracting features,establishing grey-hidden Markov models,and designing corresponding systems and other key technologies.The main research contents and results are reflected in the following aspects:(1)In order to extract the signal characteristics of the Cutting of a flexible CNC cutting machine,the health state wear process of the Cutting and the feature extraction method of the Cutting vibration data are studied.It is pointed out that the time domain,frequency domain and wavelet methods are the main methods for feature extraction.(2)Based on the effective characteristics of the Cutting,the state of the Cutting divided by the maximum entropy weighted fuzzy C-means clustering algorithm is proposed.In order to solve the large-variety and large-volatility observation data of the sample,grey hidden Markov forecasting model based on comprehensive grey theory model and HMM algorithm was established.The experimental verification of the gray-hidden Markov model method has a significant effect on prediction accuracy and convergence speed.(3)Based on the theoretical research of PHM technology,design the health evaluation system for the Cutting of the flexible material CNC cutting machine,and build an experimental platform and operation interface.The implementation scheme of Cutting health evaluation system for flexible material CNC cutting machine was expounded.Through experiments,it was verified that the Cutting system of the flexible material CNC cutting machine proposed in this paper provides a reference scheme for fault diagnosis and health prediction of flexible materials.