IR
State recognition of milling tool wear based on time series analysis and fuzzy cluster
Xu, Chuangwen; Chen, Hualing; Cheng, Zhongwen; Li, Baodong
2007-08-01
发表期刊Jixie Qiandu/Journal of Mechanical Strength
ISSN10019669
卷号29期号:4页码:525-531
摘要Based on time series analysis and fuzzy cluster analysis, a new method of state recognition of milling tool wear was set up. 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.
收录类别EI
语种中文
出版者Journal of Mechanical Strength, Zhengzou, Henan, 450052, China
EI入藏号20073610803011
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/151458
专题兰州理工大学
作者单位1.School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2.Department of Mechanical Engineering, Lanzhou Polytechnic College, Lanzhou 730050, China
推荐引用方式
GB/T 7714
Xu, Chuangwen,Chen, Hualing,Cheng, Zhongwen,et al. State recognition of milling tool wear based on time series analysis and fuzzy cluster[J]. Jixie Qiandu/Journal of Mechanical Strength,2007,29(4):525-531.
APA Xu, Chuangwen,Chen, Hualing,Cheng, Zhongwen,&Li, Baodong.(2007).State recognition of milling tool wear based on time series analysis and fuzzy cluster.Jixie Qiandu/Journal of Mechanical Strength,29(4),525-531.
MLA Xu, Chuangwen,et al."State recognition of milling tool wear based on time series analysis and fuzzy cluster".Jixie Qiandu/Journal of Mechanical Strength 29.4(2007):525-531.
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