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Research on Tool Wear Monitoring of Milling Cutter Based on Co-integration

Author: LiQiMing
Tutor: WangGuoFeng
School: Tianjin University
Course: Mechanical Manufacturing and Automation
Keywords: Milling Cutting force Cointegration modeling Support Vector Machine Hidden Markov Artificial Neural Networks BP
CLC: TG54
Type: Master's thesis
Year: 2010
Downloads: 55
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Abstract


Milling is one of the commonly used processing methods , its state of wear of the tool directly affect the machining of the workpiece surface quality and dimensional accuracy . For on-line monitoring of the cutter wear , this paper the cointegration modeling method to achieve the parametric modeling of the cutting force signal and intelligent diagnostic technology to achieve the classification of tool wear . The specific contents are as follows : cutting force signal with cointegration model of the milling process conducted in-depth research . The shortcomings of the current tool condition monitoring nonparametric feature extraction can not be given a precise mathematical model , generalized unit root test , co-integration test and cointegration vector estimation , different wearing cutting force signal cointegration , the dynamic changes in order to more clearly reflect the state of wear . Based on the stationary test of co-integration process of cutting force signal identification of tool wear . Milling force signal periodic time-varying characteristics , this paper into three state of wear of the milling force signal on behalf of the other two wear state co-integration model , the stationary test , the co-integration process by determining its smooth to achieve the identification of the different state of wear . Studies have shown that this identification method can effectively avoid the milling force signal cycle time-varying interference . Based on the cointegration vector characteristics , using support vector machine classification techniques cutter wear state identification . Different wearing cointegration vector , respectively, using artificial neural network BP , Hidden Markov and support vector machine model for training and recognition . The results show that a small sample of low-dimensional features , support vector machine to identify more effective than the other two models . Above studies have shown , the cointegration modeling method can be more accurately reflect the dynamic nature of tool wear . At the same time , the co-integration modeling combined with support vector machine model can be more accurate in the conditions of low-dimensional features of the small sample identification tool wear state . These studies are carried out to improve the accuracy of tool wear monitoring and processing efficiency and quality of the workpiece at the cutting process is of great significance .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal cutting and machine tools > Milling and milling machine
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