Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy

M. Prakash, M. Kanthababu, K. P. Rajurkar

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.

Original languageEnglish (US)
Pages (from-to)1499-1511
Number of pages13
JournalInternational Journal of Advanced Manufacturing Technology
Volume77
Issue number5-8
DOIs
StatePublished - Jan 1 2015

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Acoustic emissions
Aluminum alloys
Wear of materials
Frequency domain analysis
Time domain analysis
Shearing
Frequency bands
Machining
Surface roughness

Keywords

  • Acoustic emission
  • Chip formation mechanism
  • Chip morphology
  • Discrete wavelet transformation
  • Fast Fourier transformation
  • Microendmilling
  • Tool wear

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

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title = "Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy",
abstract = "This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.",
keywords = "Acoustic emission, Chip formation mechanism, Chip morphology, Discrete wavelet transformation, Fast Fourier transformation, Microendmilling, Tool wear",
author = "M. Prakash and M. Kanthababu and Rajurkar, {K. P.}",
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AU - Kanthababu, M.

AU - Rajurkar, K. P.

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N2 - This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.

AB - This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.

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KW - Fast Fourier transformation

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