Surface roughness monitoring by singular spectrum analysis of vibration signals

被引：82

作者：

Garcia Plaza, E. ^{[1
]}

Nunez Lopez, P. J. ^{[1
]}

机构：

[1] Univ Castilla La Mancha, Higher Tech Sch Ind Engn, Energy Res & Ind Applicat Inst INEI, Avda Camilo Jose Cela S-N, Ciudad Real 13003, Spain

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2017年 / 84卷

关键词：

Singular spectrum analysis (SSA); Vibrations signals; Surface roughness monitoring; CNC turning; TOOL WEAR; DIMENSIONAL DEVIATION; CUTTING PARAMETERS; PREDICTION; CHATTER; SYSTEM;

D O I：

10.1016/j.ymssp.2016.06.039

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：516 / 530

页数：15

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