Milling tool wear state recognition based on partitioning around medoids (PAM) clustering

2016 ◽  
Vol 88 (5-8) ◽  
pp. 1203-1213 ◽  
Author(s):  
Zhimeng Li ◽  
Guofeng Wang ◽  
Gaiyun He
Keyword(s):  
Tool Wear ◽  
State Recognition ◽  
Partitioning Around Medoids ◽  
Milling Tool

scholarly journals Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Yang Hui ◽  
Xuesong Mei ◽  
Gedong Jiang ◽  
Tao Tao ◽  
Changyu Pei ◽  
...  
Keyword(s):  
Tool Wear ◽  
Frequency Domain ◽  
Recognition Accuracy ◽  
Milling Process ◽  
Ensemble Model ◽  
Vibration Signals ◽  
State Recognition ◽  
Model Based ◽  
Milling Tool ◽  
Accuracy And Stability

Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G-mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

State Recognition Technology and Application on Milling Tool Wear

2007 ◽  
Vol 10-12 ◽  
pp. 869-873
Author(s):  
Chuang Wen Xu ◽  
Hua Ling Chen ◽  
Z. Liu
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Autocorrelation Function ◽  
Feature Vector ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
State Recognition ◽  
Milling Tool

A new method of state recognition of milling tool wear was presented based on time series analysis and fuzzy cluster analysis. After calculating, verifying liberation signal of tool state, and analyzing cutoff property, trailing property, periodicity of the sample autocorrelation function and partial autocorrelation function as well as estimating parameter of model. It can be decided that dynamic data serial is suit AR(p) (autoregression) model. Taking p equal to 12 as a feature vector extraction, based on the fuzzy cluster analysis the similarity relation between the feature vector of the tool working state and the sample feature vector was obtained. Working state of tool wear was determined according to the similarity relation of feature vector. This method was used to recognize initial wear state, normal wear state and acute wear state of milling tool. The result indicates that this method of tool wear recognition based on time series analysis and fuzzy cluster is effective.

Investigation of microstructured milling tool for deferring tool wear

Wear ◽  
2011 ◽  
Vol 271 (9-10) ◽  
pp. 2433-2437 ◽  
Author(s):  
Wenlong Chang ◽  
Jining Sun ◽  
Xichun Luo ◽  
James M. Ritchie ◽  
Chris Mack
Keyword(s):  
Tool Wear ◽  
Milling Tool

Research on Tool Wear Form in Micro Turn-Milling Process

2012 ◽  
Vol 184-185 ◽  
pp. 663-667 ◽  
Author(s):  
Lin Hui Zhao ◽  
Jian Cheng Zhang ◽  
Wei Su
Keyword(s):  
Tool Wear ◽  
Small Diameter ◽  
Milling Process ◽  
Milling Tool ◽  
Key Factor ◽  
Part Surface ◽  
End Mills ◽  
Series Of Experiments ◽  
Turn Milling ◽  
Wear Condition

In micro machining, turn-milling tool wear is a key factor for part surface quality. This paper carries on experiments on end mills wear in micro turn-milling machining, aiming to research the wear form and provide some reference data for developing wear standard of small diameter end mills. To measure wear condition of end mills, machine vision technique is utilized. This paper designs and sets up an online end mill wear measurement system for a micro turn-milling process center. With a series of experiments on small diameter end mills, wear conditions of different cutting positions are researched. Based on analysis of experiment data, wear characteristics and wear rule for micro turn-milling process are summarized in this paper.

Precision Cutting of Structured Ceramic Molds with Micro PCD Milling Tool

2011 ◽  
Vol 5 (3) ◽  
pp. 277-282 ◽  
Author(s):  
Hirofumi Suzuki ◽  
◽  
Tatsuya Furuki ◽  
Mutsumi Okada ◽  
Katsuji Fujii ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Tungsten Carbide ◽  
Polycrystalline Diamond ◽  
Micro Milling ◽  
Grinding Wheels ◽  
Form Accuracy ◽  
Structured Surfaces ◽  
Milling Tool ◽  
Milling Tools

Micro milling tools made of PolyCrystalline Diamond (PCD) have been developed to machine ceramic micro dies and molds. Cutting edges are ground with diamond wheels. PCD milling tool wear is evaluated by cutting binder-less tungsten carbide spherical molds and machining structured surfaces for trial. Results of experiments clarified that PCD milling tool life is over 10 times that of resinoid diamond grinding wheels, and that form accuracy was 0.1 µm-0.3 µm P-V and surface roughness was 10 nm Rz.

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