Investigating galling wear behaviour in sheet metal stamping using acoustic emissions

Shanbhag, Vignesh Vishnudas; Rolfe, Bernard; Arunachalam, N.; Pereira, Michael

doi:10.1016/j.wear.2018.07.003

Cited by 30 publications

(42 citation statements)

References 34 publications

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“…For the unworn tool, as shown in Figure 13a, the power of the AE burst waveform is distributed throughout the time span in the frequency range of 0.04-0.3 MHz. This AE frequency range is attributed to the plastic deformation of the sheet in the stamping process [15]. In Figure 13b, the maximum intensity of power distribution increases in the AE frequency range of 0.04-0.3 MHz (yellow colour in spectrogram).…”

Section: Frequency Characteristics Of the Ae Burst Waveform During Wementioning

confidence: 92%

“…The AE sampling frequency for both the tests conditions was maintained at 2 MHz per channel. To verify the contact between the AE sensors and the samples, the Hsu-Nielsen pencil lead break test was performed at the start of each test, as described in our previous studies [11,15]. ( Figure 6b).…”

Section: Scratch Testmentioning

confidence: 99%

“…The reconstructed AE signal was further analysed using Hilbert Huang Transform (HHT). To understand the magnitude of the frequency peak with increase in wear severity, frequency-magnitude plots were obtained using the reconstructed IMFs from the HHT analysis [15]. In addition, the AE bandpower and AE mean-frequency was calculated for the reconstructed AE burst waveform using an inbuilt function available in the MATLAB software [24,25].…”

Section: Analysis Of the Ae Burst Waveformmentioning

confidence: 99%

“…The relative cumulative spectral power index feature of the audio signal was proposed to monitor progressive wear behaviour of stamping tool. Skåre et al [14] and Shanbhag et al [15] studied progressive behaviour of stamping tool using AE features. Skåre et al [14] used AE RMS to monitor progressive behaviour of the stamping tool and Shanbhag et al [15] used AE mean-frequency to investigate wear initiation on the stamping tool at the microscopic level.…”

Section: Introductionmentioning

confidence: 99%

“…Skåre et al [14] and Shanbhag et al [15] studied progressive behaviour of stamping tool using AE features. Skåre et al [14] used AE RMS to monitor progressive behaviour of the stamping tool and Shanbhag et al [15] used AE mean-frequency to investigate wear initiation on the stamping tool at the microscopic level. Filippov et al [16][17][18] studied the correlation between the AE frequency characteristics and the wear in the metal cutting process.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics

2020

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In the sheet metal stamping process, during sliding contact between the tool and sheet, it is expected that severe events such as tool wear or fracture on the sheet generate acoustic emission (AE) burst waveforms. Attempts have been made in the literature to correlate the AE burst waveform with the wear mechanisms. However, there is a need for additional studies to understand the frequency characteristics of the AE burst waveform due to the severity and progression of the galling wear. This paper will determine the AE frequency characteristics that can be used to monitor galling wear, independent of the experimental process examined. The AE burst waveforms generated during the stamping and scratch tests are analysed in this paper to understand the change in the AE frequency characteristics with the galling severity. These AE burst waveforms were investigated using the Hilbert Huang Transform (HHT) time-frequency technique, band power, and mean-frequency. Subsequently, these AE frequency features are correlated with the wear behaviour observed via high-resolution profilometer images of the stamped parts and scratch surfaces. Initially, the HHT technique is applied to the AE burst waveform to understand the influence of wear severity in the power distribution over the wide AE frequency range. Later, the AE bandpower feature is used to quantitatively analyse the power in each frequency interval during the unworn and worn tool conditions. Finally, the mean-frequency of AE signal is identified to be able to determine the onset of galling wear. The new knowledge defined in this paper is the AE frequency features and wear measurement feature that can be used to indicate the onset of galling wear, irrespective of the processes examined. Highlights •AE burst waveform can be used to investigate galling. • AE mean-frequency can indicate galling wear initiation at industrial and tribological condition. • Profile depth wear feature can be used quantitatively to correlate with AE frequency features. • Frequency-magnitude and bandpower increased with increase in galling severity.

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Section: Frequency Characteristics Of the Ae Burst Waveform During Wementioning

confidence: 92%

Section: Scratch Testmentioning

confidence: 99%

Section: Analysis Of the Ae Burst Waveformmentioning

confidence: 99%