Tool condition monitoring in turning using acoustic emission

Chockalingam, Palanisamy; Ervina, Efzan M.N.; Prabhu, C. M. R.

doi:10.1063/1.5066951

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…There are many parameters of AE. Chockalingam et al [ 35 ] considered only amplitude in their investigations but the authors chose other patameters for further analysis i.e., amplitude, RMS, AE hits and frequency. The analysis of those parameters was made using AE Win for USB Version E5.30 software.…”

Section: Resultsmentioning

confidence: 99%

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

Dudzik

Labuda

2020

Materials

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Ensuring optimal turning conditions has a huge impact on the quality and properties of the machined surface. The condition of the cutting tool is one of the factors to achieve this goal. In order to control its wear during the turning process, monitoring was used. In this study, the acoustic emission method and measure of cutting forces during turning were used for monitoring that process. The research was carried out on a universal lathe center (CU500MRD type) using a Kistler dynamometer with assembled removable insert CCET09T302R-MF by DIJET Industrial CO., LTD. A dynamometer allows to measure forces Fx (radial force), Fy (feed force) and Fz (cutting force). The turning process was performed on a shaft with 60 mm diameter made of 304L stainless steel. The AE research was carried at Physical Acoustics Corporation with the kit that includes: recorder USB AE Node, preamplifier, AE-sensor VS 150M and computer with dedicated software used for recording and analyzing AE data. The aim of this paper is to compare selected diagnostic methods: acoustic emission and cutting forces measurement for monitoring wear of cutting tool edge. Analysis of the research results showed that both selected methods of monitoring the turning process allowed the determination of the beginning of the tool damage process.

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Section: Resultsmentioning

confidence: 99%

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

Dudzik

Labuda

2020

Materials

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“…Chockalingam et al. 13 mounted the AE sensor at a distance of 4 cm from the tool tip during the turning of Aluminium 6061 alloy and the sound intensity obtained are around 80 decibels which is comparatively high. Different authors arrived at different signal amplitude values and the lack of an optimized distance may be one of the reasons for these inconsistencies.…”

Section: Methodsmentioning

confidence: 99%

“…12 The correlation of the tool conditions with the process parameters described by Chockalingam et al. 13 indicates the significance of the AE on turning 14 of aluminium alloy. Both the parameters like spindle speed and feed rate play a vital part in the detection of AE but the most significant factor is spindle speed.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Flank wear prediction in tungsten carbide tool using acoustic signal amplitude in conventional turning operation

Sreeraj

Elias

Manu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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A tool wear prediction system that senses the cutting tool wear and allows for the optimum utilization of the tool is a deciding factor in achieving good surface quality of machined products. In recent years, the use of acoustic emission signals that emerged during machining operations has gained great significance in accurately predicting the tool condition. The objective of this work is to determine the variation in acoustic signal characteristics with tool flank wear in conventional turning process using Fast Fourier Transformation (FFT). Unlike the previous works, the experiments in this work were performed after optimizing the distance of the sensor position from the tip of the tool, since the optimum position of the sensor determines the transmission of the acoustic signals with minimum attenuation. The obtained raw signal output of the acoustic sensor is processed using FFT and the spectral analysis of the signal is carried out to determine the frequencies and also the magnitude of signal content at each frequency. This study establishes a correlation between the flank wear and the amplitude of acoustic signals. The model is validated using three sets of experiments at various cutting conditions. The average percentage deviation of the predicted tool wear from the experimental results is found to be 9.97%. This study indicates that the AE signals can be used to model the flank wear progression and can be useful for the accurate flank wear prediction in machining processes.

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