Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring | Zendy

Juan C. Jauregui | Zendy; Juvenal R. Resendiz | Zendy; Suresh Thenozhi | Zendy; Tibor Szalay | Zendy; Adam Jacso | Zendy; Marton Takacs | Zendy

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Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

Author(s) -

Juan C. Jauregui,

Juvenal R. Resendiz,

Suresh Thenozhi,

Tibor Szalay,

Adam Jacso,

Marton Takacs

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2797003

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Tool condition monitoring systems are essential in micromilling applications. A tool's slenderness requires high-precision monitoring systems for online measurements. In most cases, tool health is indirectly estimated by processing and analyzing the cutting process parameters. In that sense, the main challenge lies in the proper selection of the process parameters and their processing techniques, so that a robust and accurate assessment of the tool's health is obtained. This paper proposes a frequencyand time-frequency-based analysis of cutting force and vibration signals for estimating the tool condition of a high-speed micromilling process. Measurements obtained from different cutting conditions were utilized in the analysis. The results indicate variations in the dominant frequencies, which result from tool wear. Furthermore, it is important to note that the analysis results obtained from the two process signals provide more reliable results and improve the sensing bandwidth.

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