Skip to main content
SpringerLink
Log in

Tool breakage monitoring based on sequential hypothesis test in ultrasonic vibration-assisted drilling of CFRP

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Tool condition is highly relative to the productivity, quality, and safety of ultrasonic vibration-assisted drilling (UVAD) of carbon fiber-reinforced polymer (CFRP). Tool breakage can cause the degradation of drilling quality and maybe even lead to unexpected machine downtime. Therefore, tool breakage monitoring is the key technique of ensuring drilling quality and realizing fully automated drilling. However, existing tool breakage monitoring methods based on machine learning need training model previously, which is impractical for the actual drilling process. In this work, a novel tool breakage monitoring method based on a sequential probability ratio test (SPRT) in UVAD of CFRP is proposed. Three different damage levels are introduced to simulate the tool breakage in the drilling process. The vibration signals collected under different tool damage levels in the experiment are preprocessed by low-pass filtering to remove the disturbance frequency generated by the ultrasonic spindle system. To reduce data redundancy, the signals are downsampled according to the useful frequency band and the feature parameter extracted from test signals is finally fed into the SPRT model as a test sequence to recognize tool damage levels. Root mean square error (RMSE) between the same conditions and between different types of conditions was selected as the criteria to evaluate the reliability of the method. The test results and error analysis show that the method is effective and reliable to classify different tool breakage conditions during UVAD of CFRP.

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

Similar content being viewed by others

Availability of data and material

All data generated or analyzed during this study are included in this article and its additional files.

Code availability

Not applicable.

References

  1. Alessandra C (2018) Machining of fibre reinforced plastic composite materials. Materials 11(3):442

    Article  Google Scholar 

  2. Lee JH, Ge JC, Song JH (2021) Study on burr formation and tool wear in drilling CFRP and its hybrid composites. Appl Sci 11(1):384

    Article  Google Scholar 

  3. Nguyen D, Bin Abdullah MS, Khawarizmi R, Kim D, Kwon P (2020) The effect of fiber orientation on tool wear in edge-trimming of carbon fiber reinforced plastics (CFRP) laminates. Wear 450–451:203213

    Article  Google Scholar 

  4. Wang D, Jiao F, Mao X (2020) Mechanics of thrust force on chisel edge in carbon fiber reinforced polymer (CFRP) drilling based on bending failure theory. Int J Mech Sci 169:105336

    Article  Google Scholar 

  5. Amini S, Baraheni M, Mardiha A (2017) Parametric investigation of rotary ultrasonic drilling of carbon fiber reinforced plastics. Proc Inst Mech Eng E J Process Mech Eng 232(5):540–554

    Article  Google Scholar 

  6. Hou G, Zhang K, Fan X, Luo B, Cheng H, Yan X, Li Y (2020) Analysis of exit-ply temperature characteristics and their effects on occurrence of exit-ply damages during UD CFRP drilling. Compos Struct 231:111456

    Article  Google Scholar 

  7. Fernández-Pérez J, Díaz-Álvarez J, Miguelez M, Cantero J (2021) Combined analysis of wear mechanisms and delamination in CFRP drilling. Compos Struct 255:112774

    Article  Google Scholar 

  8. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1–4):371–393

    Article  Google Scholar 

  9. Patra K, Jha AK, Szalay T, Ranjan J, Monostori L (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng 48:279–291

    Article  Google Scholar 

  10. Geramifard O, Xu JX, Zhou JH, Li X (2014) Multimodal hidden Markov model-based approach for tool wear monitoring. IEEE Trans Industr Electron 61(6):2900–2911

    Article  Google Scholar 

  11. Klocke F, Döbbeler B, Pullen T, Bergs T (2019) Acoustic emission signal source separation for a flank wear estimation of drilling tools. Procedia CIRP 79:57–62

    Article  Google Scholar 

  12. Simon GD, Deivanathan R (2019) Early detection of drilling tool wear by vibration data acquisition and classification. Manuf Lett 21:60–65

    Article  Google Scholar 

  13. Tsanakas JA, Botsaris PN, Amirids IG, Galeridis GG (2012) Evaluation of sensor-based condition monitoring methods as in-process tool wear and breakage indices-case study: drilling. Diagnostyka 2(62):3–13

  14. Ratava J, Lohtander M, Varis J (2017) Tool condition monitoring in interrupted cutting with acceleration sensors. Robot Comput Integr Manuf 47:70–75

    Article  Google Scholar 

  15. Lu Z, Wang M, Dai W, Sun J (2019) In-process complex machining condition monitoring based on deep forest and process information fusion. Int J Adv Manuf Technol 104(5):1953–1966

    Article  Google Scholar 

  16. Kang G-S, Kim S-G, Yang G-D, Park K-H, Lee DY (2019) Tool chipping detection using peak period of spindle vibration during end-milling of inconel 718. Int J Precis Eng Manuf 20(11):1851–1859

    Article  Google Scholar 

  17. Wald A (1945) Sequential tests of statistical hypotheses. Ann Math Stat 16(2):117–186

    Article  MathSciNet  Google Scholar 

  18. Chetouani Y (2014) A sequential probability ratio test (SPRT) to detect changes and process safety monitoring. Process Saf Environ Prot 92(3):206–214

    Article  Google Scholar 

  19. Goodman NA, Venkata PR, Neifeld MA (2007) Adaptive waveform design and sequential hypothesis testing for target recognition with active sensors. IEEE J Sel Topics Signal Proc 1(1):105–113

    Article  Google Scholar 

  20. Chen H, Shang Y, Sun K (2013) Multiple fault condition recognition of gearbox with sequential hypothesis test. Mech Syst Signal Process 40(2):469–482

    Article  Google Scholar 

  21. Li D, Zhang M, Lai Z, Shen Y (2013) Sequential probability ratio test based fault detection method for actuators in gnc system. In: Proceedings of the 32nd Chinese Control Conference. IEEE, pp 6324–6327

  22. Huang W, Cao S, Li HN, Zhou Q, Wu C, Zhu D, Zhuang K (2021) Tool wear in ultrasonic vibration–assisted drilling of CFRP: a comparison with conventional drilling. Int J Adv Manuf Technol 115(5):1809–1820

    Article  Google Scholar 

  23. Nakandhrakumar RS, Dinakaran D, Satishkumar S, Gopal M (2014) Influence of sensor positioning in tool condition monitoring of drilling process through vibration analysis. Adv Mater Res 984–985:564–569

    Article  Google Scholar 

  24. Claudiu F, Bisu M, Zapciu O, Cahuc A, Gérard M (2012) Envelope dynamic analysis: a new approach for milling process monitoring. Int J Adv Manuf Technol 62(5–8):471–486

    Google Scholar 

  25. Tsanakas I, Botsaris P, Galeridis G (2012) Evaluation of sensor-based condition monitoring methods as in-process tool wear and breakage indices – case study: drilling. Diagnostyka: Applied Structural Health Usage and Condition Monitoring 2:3–15

    Google Scholar 

  26. Rafezi H, Akbari J, Behzad M (2012) Tool condition monitoring based on sound and vibration analysis and wavelet packet decomposition. In: 2012 8th International Symposium on Mechatronics and its Applications. IEEE, pp 1–4

  27. Kilundu B, Dehombreux P, Chiementin X (2011) Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech Syst Signal Process 25(1):400–415

    Article  Google Scholar 

  28. Gao G, Zhong Y, Gao S, Gao B (2021) Double-channel sequential probability ratio test for failure detection in multisensor integrated systems. IEEE Trans Instrum Meas 70:1–14

    Google Scholar 

  29. Syafiq H, Kamarizan M, Ghazali MF, Yusoff AR (2015) Statistical analysis of deep drilling process conditions using vibrations and force signals. MATEC Web of Conferences 74:00002

    Article  Google Scholar 

Download references

Funding

The work described in this paper is supported by the State Key Lab of Digital Manufacturing Equipment and Technology (Grant No: DMETKF2020026) and the Key Research and Development Project of Hubei Province (Grant No: 2020BAB033).

Author information

Authors and Affiliations

  1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Wenjian Huang, Shiyu Cao, Qi Zhou & Chaoqun Wu

  2. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chaoqun Wu

Authors
  1. Wenjian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiyu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaoqun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wenjian Huang: writing-original draft preparation, methodology, data curation; Shiyu Cao: investigation, formal analysis, resources; Qi Zhou: investigation, validation; Chaoqun Wu: reviewing and editing, funding acquisition, conceptualization, supervision.

Corresponding author

Correspondence to Chaoqun Wu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, W., Cao, S., Zhou, Q. et al. Tool breakage monitoring based on sequential hypothesis test in ultrasonic vibration-assisted drilling of CFRP. Int J Adv Manuf Technol 118, 2701–2710 (2022). https://doi.org/10.1007/s00170-021-08050-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08050-x

Keywords

Search

Navigation