Tool condition monitoring based on numerous signal features

By Krzysztof Jemielniak, Tomasz Urbanski, Joanna Kossakowska, Sebastian Bombiński. The International Journal of Advanced Manufacturing Technology, 2011

137 citations recorded by the source corpus.

This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time–frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability.

Subfields: Biomedical Engineering, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/44e058418b99325a381851b7d34a7e7903aa26d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Arjan Siddhpura
2. R. Paurobally

In Materials Chemistry

1. Arjan Siddhpura
2. R. Paurobally

In Mechanical Engineering

1. Arjan Siddhpura
2. R. Paurobally

Citations in the corpus

• A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, methodology): “[107] carried out sensor fusion using AE, vibration, and force sensors in order to analyze suitability of various signal features for tool condition monitoring.”