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Vibration-based tool wear estimation by using non-stationary Functional Series TARMA (FS-TARMA) models
Date
2017-10-01
Author
Aghdam, Behrang Hosseini
Ciğeroğlu, Ender
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Inverse problem of tool wear estimation using vibration signals is considered via non-stationary functional series time-dependent autoregressive moving average (FS-TARMA) model in this paper. The estimation procedure of FS-TARMA models is presented and through the obtained models, dynamics of the tool-holder system is identified. For finding a relationship between wear and the models, two wear sensitive features are used. First, the models are clustered considering autoregressive (AR) distance as a feature and then, damping ratios of tool-holder bending modes are used as another feature for correlating tool wear with the vibrations. The AR metric provides a parsimonious parametric way for comparison of the structures generating the time series. The obtained wear-AR distance curves possess extremums at critical wear stage. Moreover, in wear-damping ratio curves, which are obtained first time in this paper, extremums appear in the vicinity of critical wear point. These extremums can be used as a measure for tool change policy. The results of the study demonstrate the good accuracy of FS-TARMA models in prediction of tool non-stationary signals and the effectiveness of the selected features for estimation of tool major flank wear.
Subject Keywords
Damping ratio
,
AR metric
,
Vibration
,
Non-stationary
,
FS-TARMA
,
Tool wear
URI
https://hdl.handle.net/11511/38628
Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
DOI
https://doi.org/10.1007/s00170-017-0576-7
Collections
Department of Mechanical Engineering, Article