Skip to main content
SpringerLink
Log in

Application of wavelet ratio between acoustic emission and cutting force signal decomposing in intelligent monitoring of cutting tool wear when turning SKD 61

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

In this study, the authors utilize the wavelet transform technique within the intelligent turning process. This technique is applied to the machining of hardened steel SKD 61. The main focus is on monitoring the condition of the cutting tool, and this is done during the machining process, regardless of the conditions related to chip formation. Wavelet transforms serve the purpose of distinguishing indications of tool wear from received signals. These signals emerge from the analysis of components such as cutting force and acoustic emission (AE) signals. The authors investigate this analysis alongside the relationships between various cutting parameters, including depth of cut, feed rate, and cutting speed. The fifth-order wavelet transform is used to determine the ratio between the primary cutting force and the components obtained from the AE signal decomposition. This ratio is evaluated using mean–variance calculations. The intention is to mitigate the influence of different factors that relate to cutting conditions. Building upon the factors associated with cutting parameters (feed rate, depth of cut, and cutting speed), a model for predicting tool wear is established. This model takes into account the decomposition analysis ratio of the main cutting force and the AE signal component. This ratio is developed based on exponential analysis. The reliability of the proposed model for monitoring cutting tool wear is validated through a series of experiments. The results of these experiments indicate that changes in cutting parameters significantly impact the amount of tool wear. Specifically, increasing the depth of cut, feed rate, and cutting speed leads to higher cutting forces and subsequently increased tool wear. The experimental analysis for turning hardened steel SKD 61 reveals that the tool wear monitoring system introduced through wavelet analysis demonstrates impressive predictive capabilities. This system not only accurately predicts tool wear but also effectively separates noise signals from chip formation signals, even when faced with varying cutting conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Kamble, P.D., Waghmare, A.C., Askhedkar, R.D., Sahare, S.B., Patil, M., Prayagi, S.V.: Performance evaluation of CNC turning process for tool tip temperature and tool wear by Taguchi method. Mater. Today Proc. 62, 981–986 (2022)

    Article  Google Scholar 

  2. Ding, S., Zhang, H., Guo, E., Wu, W., Zhang, Y., Song, A.: Integrated roughing, finishing and chamfering turning process of toroidal worm manufacture on a general CNC lathe. J. Manuf. Process. 70, 341–349 (2021)

    Article  Google Scholar 

  3. Ntemi, M., Paraschos, S., Karakostas, A., Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: Infrastructure monitoring and quality diagnosis in CNC machining: a review. CIRP J. Manuf. Sci. Technol. 38, 631–649 (2022)

    Article  Google Scholar 

  4. Tangjitsitcharoen, S.: In-process monitoring and detection of chip formation and chatter for CNC turning. J. Mater. Process. Technol. 209, 4682–4688 (2009)

    Article  Google Scholar 

  5. Naveen Kumar Reddy, R., Lokesh, S.: A comparative study on physical vapor deposition—titanium carbide coated novel insert and uncoated carbide insert in CNC turning of EN24 for minimizing surface roughness and maximizing material removal rate. Mater. Today Proc. 69, 858–862 (2022)

    Article  Google Scholar 

  6. Swain, S., Kumar, R., Panigrahi, I., Sahoo, A.K., Panda, A.: Machinability performance investigation in CNC turning of Ti–6Al–4V alloy: dry versus iron–aluminium oil coupled MQL machining comparison. Int. J. Lightweight Mater. Manuf. 5, 496–509 (2022)

    Google Scholar 

  7. Bahador, A., Du, C., Ng, H.P., Dzulqarnain, N.A., Ho, C.L.: Cost-effective classification of tool wear with transfer learning based on tool vibration for hard turning processes. Measurement 201, 111701 (2022)

    Article  Google Scholar 

  8. Kim, D.M., Kim, H.I., Park, H.W.: Tool wear, economic costs, and CO2 emissions analysis in cryogenic assisted hard-turning process of AISI 52100 steel. Sustain. Mater. Technol. 30, e00349 (2021)

    Google Scholar 

  9. Orra, K., Choudhury, S.K.: Development of flank wear model of cutting tool by using adaptive feedback linear control system on machining AISI D2 steel and AISI 4340 steel. Mech. Syst. Signal Process. 81, 475–492 (2016)

    Article  Google Scholar 

  10. Toubhans, B., Fromentin, G., Viprey, F., Karaouni, H., Dorlin, T.: Machinability of inconel 718 during turning: cutting force model considering tool wear, influence on surface integrity. J. Mater. Process. Technol. 285, 116809 (2020)

    Article  Google Scholar 

  11. Krajčoviech, S., Holubjak, J., Richtarik, M., Czánová, T.: Identification of process prime A turning when machining steel C56E2 and monitoring of cutting forces. Transp. Res. Procedia 55, 605–612 (2021)

    Article  Google Scholar 

  12. Yan, B., Zhu, L., Dun, Y.: Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning. J. Manuf. Syst. 61, 495–508 (2021)

    Article  Google Scholar 

  13. Nasir, V., Dibaji, S., Alaswad, K., Cool, J.: Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals. Manuf. Lett. 30, 32–38 (2021)

    Article  Google Scholar 

  14. Turhan, M.H., Tseng, G.W.G., Erkorkmaz, K., Fidan, B.: Dynamic model identification for CNC machine tool feed drives from in-process signals for virtual process planning. Mechatronics 72, 102445 (2020)

    Article  Google Scholar 

  15. Dutta, S., Pal, S.K., Sen, R.: Progressive tool flank wear monitoring by applying discrete wavelet transform on turned surface images. Measurement 77, 388–401 (2016)

    Article  Google Scholar 

  16. Seid Ahmed, Y., Arif, A.F.M., Veldhuis, S.C.: Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining. Measurement 154, 107478 (2020)

    Article  Google Scholar 

  17. Li, B., Wang, E., Shang, Z., Li, Z., Li, B., Liu, X., et al.: Deep learning approach to coal and gas outburst recognition employing modified AE and EMR signal from empirical mode decomposition and time-frequency analysis. J. Nat. Gas Sci. Eng. 90, 103942 (2021)

    Article  Google Scholar 

  18. Spinosa, E., Iafrati, A.: A noise reduction method for force measurements in water entry experiments based on the ensemble empirical mode decomposition. Mech. Syst. Signal Process. 168, 108659 (2022)

    Article  Google Scholar 

  19. Deng, B., He, Q., DePaiva, J.M., Veldhuis, S.C.: A novel approach to cutting tool edge design based on initial wear stage. J. Mater. Process. Technol. 304, 117561 (2022)

    Article  Google Scholar 

  20. Zhang, X., Gao, Y., Guo, Z., Zhang, W., Yin, J., Zhao, W.: Physical model-based tool wear and breakage monitoring in milling process. Mech. Syst. Signal Process. 184, 109641 (2023)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, Hanoi University of Industry, 298 Cau Dien, Bac Tu Liem District, Hanoi, Vietnam

    Dung Hoang Tien, Pham Thi Thieu Thoa & Trinh Nguyen Duy

Authors
  1. Dung Hoang Tien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pham Thi Thieu Thoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trinh Nguyen Duy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Trinh Nguyen Duy.

Ethics declarations

Conflict of interest

The authors have nothing to disclose and no conflict of interests regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tien, D.H., Thoa, P.T.T. & Duy, T.N. Application of wavelet ratio between acoustic emission and cutting force signal decomposing in intelligent monitoring of cutting tool wear when turning SKD 61. Int J Interact Des Manuf 18, 525–539 (2024). https://doi.org/10.1007/s12008-023-01571-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-023-01571-7

Keywords

Search

Navigation