Ploughing detection in micromilling processes using the cutting force signal

Hui Taek Yun; Segon Heo; Min Kyu Lee; Byung Kwon Min; Sang Jo Lee

doi:10.1016/j.ijmachtools.2011.01.003

Ploughing detection in micromilling processes using the cutting force signal

Hui Taek Yun, Segon Heo, Min Kyu Lee, Byung Kwon Min, Sang Jo Lee

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

42 Citations (Scopus)

Abstract

A new method is presented for detecting the onset of ploughing during micromilling on the basis of the cutting force signal. The method utilises the variation in the peak values of the cutting force. Multiresolution analysis with the undecimated wavelet transform is used to detect the peak values of the cutting force signal. A ploughing-detection algorithm is developed, which compares the normalised variance in peak values with preset thresholds. The proposed method is verified by comparing the surface roughness of the sidewall under ploughing and normal cutting conditions in sidewall-machining experiments with a micromilling tool.

Original language	English
Pages (from-to)	377-382
Number of pages	6
Journal	International Journal of Machine Tools and Manufacture
Volume	51
Issue number	5
DOIs	https://doi.org/10.1016/j.ijmachtools.2011.01.003
Publication status	Published - 2011 May

Bibliographical note

Funding Information:
This work was supported by the Large Surface Micromachining System Programme of the Ministry of Knowledge Economy, Republic of Korea .

All Science Journal Classification (ASJC) codes

Mechanical Engineering
Industrial and Manufacturing Engineering

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10.1016/j.ijmachtools.2011.01.003

Cite this

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title = "Ploughing detection in micromilling processes using the cutting force signal",

abstract = "A new method is presented for detecting the onset of ploughing during micromilling on the basis of the cutting force signal. The method utilises the variation in the peak values of the cutting force. Multiresolution analysis with the undecimated wavelet transform is used to detect the peak values of the cutting force signal. A ploughing-detection algorithm is developed, which compares the normalised variance in peak values with preset thresholds. The proposed method is verified by comparing the surface roughness of the sidewall under ploughing and normal cutting conditions in sidewall-machining experiments with a micromilling tool.",

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AU - Yun, Hui Taek

AU - Heo, Segon

AU - Lee, Min Kyu

AU - Min, Byung Kwon

AU - Lee, Sang Jo

N1 - Funding Information: This work was supported by the Large Surface Micromachining System Programme of the Ministry of Knowledge Economy, Republic of Korea .

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N2 - A new method is presented for detecting the onset of ploughing during micromilling on the basis of the cutting force signal. The method utilises the variation in the peak values of the cutting force. Multiresolution analysis with the undecimated wavelet transform is used to detect the peak values of the cutting force signal. A ploughing-detection algorithm is developed, which compares the normalised variance in peak values with preset thresholds. The proposed method is verified by comparing the surface roughness of the sidewall under ploughing and normal cutting conditions in sidewall-machining experiments with a micromilling tool.

AB - A new method is presented for detecting the onset of ploughing during micromilling on the basis of the cutting force signal. The method utilises the variation in the peak values of the cutting force. Multiresolution analysis with the undecimated wavelet transform is used to detect the peak values of the cutting force signal. A ploughing-detection algorithm is developed, which compares the normalised variance in peak values with preset thresholds. The proposed method is verified by comparing the surface roughness of the sidewall under ploughing and normal cutting conditions in sidewall-machining experiments with a micromilling tool.

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Ploughing detection in micromilling processes using the cutting force signal

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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