Surface roughness identification in end milling using vibration signals and fuzzy clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

Original languageEnglish (US)
Title of host publicationDynamics, Vibration, and Control
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791850541
DOIs
StatePublished - Jan 1 2016
EventASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016 - Phoenix, United States
Duration: Nov 11 2016Nov 17 2016

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume4A-2016

Other

OtherASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016
CountryUnited States
CityPhoenix
Period11/11/1611/17/16

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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  • Cite this

    Abu-Mahfouz, I., Banerjee, A., & Rahman, A. H. M. E. (2016). Surface roughness identification in end milling using vibration signals and fuzzy clustering. In Dynamics, Vibration, and Control (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 4A-2016). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE201668207