A study to determine the onset of catastrophic wear of a processing tool by statistical parameters of acoustic emission

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.184959

Keywords:

acoustic emission, composite material, statistical amplitude parameters, mechanical processing of materials, wear of a processing tool

Abstract

In the present study, experimental research was carried out to determine the effect of the processing tool wear on the mutual change in the average statistical amplitude parameters of acoustic emission signals. Acoustic emission signals were recorded when turning silumin. The research was aimed at finding parameters to determine the occurrence of critical wear of the processing tool. It is shown that the recorded signals are continuous. The statistical amplitude parameters of the acoustic emission signals were processed under normal and catastrophic wear of the processing tool. It has been determined that the development of normal and catastrophic wear of the tool leads to a decrease in the values of the statistical amplitude parameters of the recorded signals. However, no peculiarities were found in their change to determine the developing type of wear. Meanwhile, at different time intervals, there was an anticipating increase or decrease in one of the statistical amplitude parameters of the acoustic emission signals.

The processed parameter was a coefficient characterising a mutual change in the statistical amplitude values of the recorded signals. It has been determined that absence of the tool wear is characterised by stable values of the calculated coefficient in time. The occurrence and development of catastrophic wear leads to an outburst of the value of the calculated coefficient with its subsequent accelerated decrease to the destruction of the tool. The emergence and development of normal wear leads to an increase in the value of the calculated coefficient with a subsequent transition to a sawtooth change and a gradual decrease of its size. The obtained regularities can be used when devising methods for controlling and monitoring the condition of the tool during the mechanical processing of materials, including monitoring the state of the processing tool in robotic industries

Author Biographies

Sergii Filonenko, National Aviation University Kosmonavta Komarova ave., 1, Kyiv, Ukraine, 03058

Doctor of Technical Sciences, Professor

Department of Computerized Electrotechnical Systems and Technologies

 

Anzhelika Stakhova, National Aviation University Kosmonavta Komarova ave., 1, Kyiv, Ukraine, 03058

PhD

Department of Computerized Electrotechnical Systems and Technologies

References

  1. Bhaskaran, J., Murugan, M., Balashanmugam, N., Chellamalai, M. (2012). Monitoring of hard turning using acoustic emission signal. Journal of Mechanical Science and Technology, 26 (2), 609–615. doi: https://doi.org/10.1007/s12206-011-1036-1
  2. Kulandaivelu, P., Kumar, P. S. (2012). Investigate the Crater Wear Monitoring of Single Point Cutting Tool Using Adaptive Neuro Fuzzy Inference System. Journal of Applied Science and Engineering, 15 (3), 265–274.
  3. Fadare, D. A., Sales, W. F., Bonney, J., Ezugwu, E. O. (2012). Influence of cutting parameters and tool wear on acoustic emission signal in high-speed turning of Ti-6Al-4V Alloy. Journal of Emerging Trends in Engineering and Applied Sciences, 3 (3), 547–555.
  4. Chang, L. F., Lu, M. C., Chen, K. H., Wu, C. C. (2014). Development of Condition Monitoring System for Micro Milling of PZT Deposited Si Wafer. 9th International workshop on microfactories - IWMF2014, 139–145.
  5. Qin, F., Hu, J., Chou, Y. K., Thompson, R. G. (2009). Delamination wear of nano-diamond coated cutting tools in composite machining. Wear, 267 (5-8), 991–995. doi: https://doi.org/10.1016/j.wear.2008.12.065
  6. Mascaro, B., Gibiat, V., Bernadou, M., Esquerre, Y. (2005). Acoustic emission of the drilling of Carbon/Epoxy composites. Proc. of the Forum Acusticum. Budapest, 2823–2827.
  7. Olufayo, O. A., Abou-El-Hossein, K. (2013). Acoustic Emission Monitoring in Ultra-High Precision Machining of Rapidly Solidified Aluminium. Proceedings International Conference on Competitive Manufacturing. Stellenbosch, 307–312.
  8. Prakash, M., Kanthababu, M., Gowri, S., Balasubramaniam, R., Jegaraj, J. R. (2014). Tool Condition Monitoring using Multiple Sensors Approach in theMicroendmilling of Aluminium Alloy (AA1100). 5th International & 26th All India Manufacturing Technology, Design and Research Conference - AIMTDR 2014. IIT Guwahati, Assam, 394-1–394-6.
  9. Mukhopadhyay, C. K., Jayakumar, T., Raj, B., Venugopal, S. (2012). Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 34 (2), 145–154. doi: https://doi.org/10.1590/s1678-58782012000200006
  10. Bhuiyan, M. S. H., Choudhury, I. A., Dahari, M., Nukman, Y., Dawal, S. Z. (2016). Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement, 92, 208–217. doi: https://doi.org/10.1016/j.measurement.2016.06.006
  11. Papacharalampopoulos, A., Stavropoulos, P., Doukas, C., Foteinopoulos, P., Chryssolouris, G. (2013). Acoustic Emission Signal Through Turning Tools: A Computational Study. Procedia CIRP, 8, 426–431. doi: https://doi.org/10.1016/j.procir.2013.06.128
  12. Hase, A., Wada, M., Koga, T., Mishina, H. (2013). The relationship between acoustic emission signals and cutting phenomena in turning process. The International Journal of Advanced Manufacturing Technology, 70 (5-8), 947–955. doi: https://doi.org/10.1007/s00170-013-5335-9
  13. Albers, A., Stürmlinger, T., Wantzen, K., Bartosz, Gladysz, Münke, F. (2017). Prediction of the Product Quality of Turned Parts by Real-time Acoustic Emission Indicators. Procedia CIRP, 63, 348–353. doi: https://doi.org/10.1016/j.procir.2017.03.173
  14. Abdi, H., Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2 (4), 433–459. doi: https://doi.org/10.1002/wics.101
  15. Teti, R. (2015). Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining. Procedia CIRP, 28, 3–15. doi: https://doi.org/10.1016/j.procir.2015.04.003
  16. Segreto, T., Simeone, A., Teti, R. (2013). Multiple Sensor Monitoring in Nickel Alloy Turning for Tool Wear Assessment via Sensor Fusion. Procedia CIRP, 12, 85–90. doi: https://doi.org/10.1016/j.procir.2013.09.016
  17. Kong, D., Chen, Y., Li, N., Tan, S. (2016). Tool wear monitoring based on kernel principal component analysis and v-support vector regression. The International Journal of Advanced Manufacturing Technology, 89 (1-4), 175–190. doi: https://doi.org/10.1007/s00170-016-9070-x
  18. Caggiano, A. (2018). Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition. Sensors, 18 (3), 823. doi: https://doi.org/10.3390/s18030823
  19. Filonenko, S. F. (2016). Acoustic energy at controlled cutting depth of composite material. Electronics and Control Systems, 3 (49), 93–99. doi: https://doi.org/10.18372/1990-5548.49.11244
  20. Filonenko, S. (2017). Acoustic emission at treating tool wear with a not controlled cutting depth. Proceedings of the National Aviation University, 70 (1), 90–97. doi: https://doi.org/10.18372/2306-1472.70.11426

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Published

2019-11-25

How to Cite

Filonenko, S., & Stakhova, A. (2019). A study to determine the onset of catastrophic wear of a processing tool by statistical parameters of acoustic emission. Eastern-European Journal of Enterprise Technologies, 6(9 (102), 6–11. https://doi.org/10.15587/1729-4061.2019.184959

Issue

Vol. 6 No. 9 (102) (2019): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2019 Sergii Filonenko, Anzhelika Stakhova

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