Tribological Behavior of Sheet Metal Forming Process Using Acoustic Emission Characteristics

Sindi, Cevat Teymuri; Najafabadi, Mehdi Ahmadi; Salehi, Manouchehr

doi:10.1007/s11249-013-0193-z

Cited by 17 publications

(15 citation statements)

References 21 publications

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“…In Figure 18f, the maximum amplitude of the AE burst signal reduces when compared to that of Figure 18d,e. This is mainly attributed to the dependency of the AE amplitude with the intensity of galling severity on the tool or intensity of fracture observed in the centre of the scratch [9]. From these observations, it can be confirmed that the AE burst waveform is only observed when fracture is observed at the centre of scratch [9,10].…”

Section: Ae Burst Waveform From Scratch Testmentioning

confidence: 73%

“…From Figure 11d,e, along with ploughing wear, fracture is also observed. The presence of fracture on the sheet surface is likely due to the growth and work hardening of the lump on the tool (Figure 11f) that results in fracture/cutting wear mode on the sheet surface [9,27,28]. Figure 12represents the selected AE burst waveforms from the stamping test.…”

Section: Profilometry Study Of Stamped Partsmentioning

confidence: 99%

“…In both these studies [7,8], galling wear was investigated qualitatively and force signatures were unstable when galling was observed on the tool. Sindi et al [9] performed tribological studies using acoustic emission (AE) on a SOFS tribometer and observed severe scratches and adhesive wear in the AE frequency range of 0.19-0.375 MHz. Hase et al [10] investigated adhesive and abrasive wear mechanisms using AE on pin on block setup and observed adhesive wear in the AE frequency range of 1-1.5 MHz and abrasive wear in the AE frequency range of 0.1-0.3 MHz.…”

Section: Introductionmentioning

confidence: 99%

“…Hase et al [10] investigated adhesive and abrasive wear mechanisms using AE on pin on block setup and observed adhesive wear in the AE frequency range of 1-1.5 MHz and abrasive wear in the AE frequency range of 0.1-0.3 MHz. In these studies [9,10], the AE burst waveform was mainly observed when galling was observed on the tool. Shanbhag et al [5,11], investigated ploughing, cutting, and galling wear mechanisms under controlled conditions using force and AE sensors during scratch tests.…”

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: Ae Burst Waveform From Scratch Testmentioning

confidence: 73%

Section: Profilometry Study Of Stamped Partsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics

2020

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“…Typically, AE signals are non‐stationary and nonlinear transients, whose waveforms are unknown. Hence, the other significant challenge with AE signal processing is to extract feature parameters when these involve non‐stationary AE signal sources exhibiting both time and frequency variations . Wavelet analysis is thus far the best available non‐stationary data analysis method, but may also prove to be inadequate because wavelet analysis is essentially an adjustable window Fourier spectral analysis .…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation

Zhou

Ya-nan

Chen

2016

Fatigue Fract Eng Mat Struct

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The occurrence and expansion of fatigue cracks in large wind turbine blades may lead to catastrophic blade failure. Each fatigue phase of a material has been associated with a typical set of acoustic emission (AE) signal frequency components, providing a logical base for establishing a clear connection between AE signals and the fatigue condition of a material. The relevance of efforts to relate recorded AE signals to a material's mechanical behaviour relies heavily on accurate AE signal processing. The main objective of the present study is to establish a direct correlation between the fatigue condition of a material and recorded AE signals. We introduce the blind deconvolution separation (BDS) approach because the result of AE monitoring is usually a convoluted mixture of signals from multiple sources. The method is implemented on data acquired from a fatigue test rig employing a wind turbine blade with an artificial transverse crack seeded in the surface at the base of the blade. Two different sets of fatigue loading were conducted. The convoluted signals are collected from the AE acquisition system, and the weak crack feature is extracted and analysed based on the BDS algorithm. The study reveals that the application of BDS‐based AE signal analysis is an appropriate approach for distinguishing and interpreting the different fatigue damage states of a wind turbine blade. The novel methodology proposed for fatigue crack identification will allow for improved predictive maintenance strategies for the glass‐epoxy blades of wind turbines. The experimental results clearly demonstrate that the AE signals generated by a fatigue crack on a wind turbine blade can be synchronously separated and identified. Characterizing and assessing fatigue conditions by AE monitoring based on BDS can prevent catastrophic failure and the development of secondary defects, as well as reduce unscheduled downtime and costs. The possibility of using AE monitoring to assess the fatigue condition of fibre composite blades is also considered.

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Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

Griffin

Shanbhag

Pereira

et al. 2021

Int J Adv Manuf Technol

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Galling wear in sheet metal stamping processes can degrade the product quality and adversely affect mass production. Studies have shown that acoustic emission sensors can be used to measure galling. In the literature, attempts have been made to correlate the acoustic emission features and galling wear in the sheet metal stamping process. However, there is very little attempt made to implement machine learning techniques to detect acoustic emission features that can classify non-galling and galling wear as well as provide additional wear-state information in the form of strong visualisations. In the rst part of the paper time domain and frequency domain analysis are used to determine the acoustic emission features that can be used for unsupervised classi cation. Due to galling wear progression on the stamping tools, the behaviour of acoustic emission waveform changes from stationary to a nonstationary state. The initial change in acoustic emission waveform behaviour due to galling wear initiation is very di cult to observe due to the ratio of change against the large data size of the waveform. Therefore, a time-frequency technique "Hilbert Huang Transform" is applied to the acoustic emission waveform as that is sensitive to change of wear state, and is used for the classi cation of 'non galling' and the 'transition of galling'. Also, the unsupervised learning algorithm fuzzy clustering is used as comparison against the supervised learning techniques. Despite not knowing a priori the wear state labels, fuzzy clustering is able to de ne three relatively accurate distinct classes: "unworn", "transition to galling", and "severe galling". In the second part of the paper, the acoustic emission features are used as an input to the supervised machine learning algorithms to classify acoustic emission features related to non-galling and galling wear. An accuracy of 96% was observed for the prediction of non-galling and galling wear using Classi cation, Regression Tree (CART) and Neural Network techniques. In the last part, a reduced Short Time Fourier Transform of top 10 absolute maximum component acoustic emission feature sets that correlates to wear measurement data "pro le depth" is used to train and test supervised Neural Network and CART algorithms. The algorithms predicted the pro le depth of 530 unseen parts (530 unseen cases), which did not have any associated labelled depth data. This shows the power of using machine learning techniques that can use a small data training set to provide additional predicted wear-state on a much larger data set. Furthermore, the machine learning techniques presented in this paper can be used further to develop a real-time measurement system to detect the transition of galling wear from measured acoustic emission features.

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Tribological Behavior of Sheet Metal Forming Process Using Acoustic Emission Characteristics

Cited by 17 publications

References 21 publications

Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics

Investigation of Galling Wear Using Acoustic Emission Frequency Characteristics

Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation

Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

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