Investigation of optimal feature for milling chatter identification using supervised machine learning techniques

Mishra, Rohit; Kiran, Matta S.N.S.; Maheswaram, Manikantadhar; Upadhyay, Akshat; Singh, Bhagat

doi:10.1016/j.jer.2023.100138

Cited by 3 publications

(1 citation statement)

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“…A statistical indicator called RMS is used to describe the amplitude or magnitude of a signal [34]. It is determined by finding the square root of the dataset's mean value for the squared values.…”

Section: Chatter Indicators and The Calculation Processmentioning

confidence: 99%

Identification of end-milling chatter based on comprehensive feature fusion

Matthew,

Cao,

Shi

2024

Eng. Res. Express

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The main barrier impeding the advancement of high-speed milling is chatter, which has a detrimental effect on the dimensional accuracy and quality of the finished workpiece. A reliable and precise chatter identification method is essential to improving the quality of machining. This paper presents a novel method for chatter identification using a comprehensive feature fusion of the Short-Time Fourier Transform (STFT) and the Fourier Synchrosqueezing Transform (FSST). The Wavelet Packet Transform (WPT) was used to pre-process the collected vibration and force signals. Wavelet packets with rich chatter information were then selected and reconstructed for further analysis. To reduce the effects of the rotating frequency and generate a hybrid spectrum with high resolution, a Gabor time-frequency filter is employed. As chatter indicators, standard deviation, skewness, and root mean square are computed. The proposed method's result shows superiority over conventional STFT and FSST across vibration and force signals, and we concluded that it is suitable and reliable for identifying chatter and useful for machining monitoring.

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Section: Chatter Indicators and The Calculation Processmentioning

confidence: 99%