Skip to main content
SpringerLink
Log in

Sensitivity of acoustic emission signals features to cutting parameters in time domain: case of milling aeronautical aluminium alloys

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

Abstract

Acoustic emission (AE) signals are thought to contain crucial information for identifying defects and monitoring processes. It is crucial to have a comprehensive understanding of how AE signal parameters behave under different experimental conditions. However, based on current research, there appears to be a lack of knowledge on the impact of machining parameters, especially in milling operations, where complex chip formation patterns, interaction effects, and directional pressures and forces are present. To bridge this informational void, analyzing how various cutting conditions impact the AE signal characteristics derived from milling operations is crucial. This research predominantly focuses on the impact of cutting conditions, material attributes, insert coatings, and nose radius on AE signal attributes in the time domain. The proposed innovative method suggests segmenting acquired AE signals correlated with the cutting tool’s trajectory through the material into three distinct phases: entry, active cutting, and exit, each marked by a particular signal timeframe for effective signal processing and characteristic derivation. Furthermore, advanced signal processing techniques and statistical analysis are utilized to determine which AE parameters are sensitive to changes in cutting parameters. This research identifies cutting speed and feed rate as the primary variables affecting AE signal characteristics. The study’s outcomes can enhance sophisticated classifications and AI techniques for monitoring machining operations.

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

References

  1. Niknam SA, Au Y, Songmene V (2013) Proposing a new acoustic emission parameter for bearing condition monitoring in rotating machines. Trans Can Soc Mech Eng 37:1105

    Article  Google Scholar 

  2. Govekar E, Gradiek J, Grabec I (2000) Analysis of acoustic emission signals and monitoring of machining processes. Ultrasonics 38:598–603

    Article  Google Scholar 

  3. Sikorska J, Mba D (2008) Challenges and obstacles in the application of acoustic emission to process machinery. Proc Inst Mech Eng, Part E: J Process Mech Eng 222:1–19

    Article  Google Scholar 

  4. XiaoQi C, Hao Z, Wildermuth D (2001) In-process tool monitoring through acoustic emission sensing. Autom Mater Process Group Autom Technol Div 1

  5. Dolinek S, Kopa J (1999) Acoustic emission signals for tool wear identification. Wear 225:295–303

    Article  Google Scholar 

  6. Iwata K, Moriwaki T (1977) An application of acoustic emission measurement to in-process sensing of tool wear. Annals CIRP 26:21–26

    Google Scholar 

  7. Chen X, Li B (2007) Acoustic emission method for tool condition monitoring based on wavelet analysis. Int J Adv Manuf Technol 33:968–976

    Article  Google Scholar 

  8. Niknam SA, Songmene V (2013) Factors governing burr formation during high-speed slot milling of wrought aluminium alloys. Proc Inst Mech Eng, Part B: J Eng Manuf 227:1165–1179

    Article  Google Scholar 

  9. Niknam SA, Songmene V (2014) Analytical modelling of slot milling exit burr size. Int J Adv Manuf Technol 73:421–432

    Article  Google Scholar 

  10. Adeniji D, Oligee K, Schoop J (2022) A novel approach for real-time quality monitoring in machining of aerospace alloy through acoustic emission signal transformation for DNN. J Manuf Mater Proc 6:18

    Google Scholar 

  11. Shen C-H (2022) Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction. Appl Acoust 196:108863

    Article  Google Scholar 

  12. Wan B-S, Lu M-C, Chiou S-J (2022) Analysis of spindle AE signals and development of AE-based tool wear monitoring system in micro-milling. J Manuf Mater Proc 6:42

    Google Scholar 

  13. Pimenov DY, Bustillo A, Wojciechowski S, Sharma VS, Gupta MK, Kuntoğlu M (2023) Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. J Intell Manuf 34:2079–2121

    Article  Google Scholar 

  14. Mian A, Driver N, Mativenga P (2011) Chip formation in microscale milling and correlation with acoustic emission signal. Int J Adv Manuf Technol 56:63–78

    Article  Google Scholar 

  15. Mohanraj T, Yerchuru J, Krishnan H, Aravind RN, Yameni R (2021) Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement 173:108671

    Article  Google Scholar 

  16. Luo M, Mei J, Zhang D (2016) Time-domain modeling of a cutter exiting a workpiece in the slot milling process. Chin J Aeronaut 29:1852–1858

    Article  Google Scholar 

  17. Anahid MJ, Heydarnia H, Niknam SA, Mehmanparast H (2021) Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis. Proc Inst Mech Eng Part B: J Eng Manuf 235(10):1558–1568. https://doi.org/10.1177/0954405420949127

  18. Gaja H, Liou F (2016) Automatic detection of depth of cut during end milling operation using acoustic emission sensor. Int J Adv Manuf Technol 86:2913–2925

    Article  Google Scholar 

  19. Nouhi S, Pour M (2021) Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding. Measurement 184:109904

  20. Twardowski P, Tabaszewski M, Wiciak–Pikuła M, Felusiak-Czyryca A (2021) Identification of tool wear using acoustic emission signal and machine learning methods. Precis Eng 72:738–744

    Article  Google Scholar 

  21. Pawade R, Joshi S (2012) Analysis of acoustic emission signals and surface integrity in the high-speed turning of Inconel 718. Proc Inst Mech Eng B J Eng Manuf 226:3–27

    Article  Google Scholar 

  22. Lee W, Ratnam M, Ahmad Z (2017) Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT). Precis Eng 47:406–423

    Article  Google Scholar 

  23. Liang S, Dornfeld D (1989) Tool wear detection using time series analysis of acoustic emission. J Eng Ind(Trans ASME) 111:199–205

    Article  Google Scholar 

  24. Tonphong K (2002) Bearing Condition Monitoring using Acoustic Emission and Vibration. Ph.D. Thesis,Brunel University, UK

    Google Scholar 

  25. Lee D, Hwang I, Valente C, Oliveira J, Dornfeld D (2006) Precision manufacturing process monitoring with acoustic emission. Int J Mach Tools Manuf 46:176–188

    Article  Google Scholar 

  26. Lee SH, Lee D (2008) In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network. Int J Prod Res 46:4871–4888

    Article  Google Scholar 

  27. Rubioa E, Tetib R, Baciub I (2006 ) Intelligent production machines and systems 2nd I*PROMS virtual international conference, 3–14, pp 1–6

  28. Niknam SA (2013) Burrs understanding, modeling and optimization during slot milling of aluminium alloys, Ph.D. Thesis, École de Technologie supérieure, Canada

  29. Tiabi A (2010) "Formation des bavures dùsinage et finition de pieces " M. Sc Thesis, M.Sc Thesis, École de technologie superieure, Canada

    Google Scholar 

  30. Niknam SA, Songmene V (2017) Burr formation and correlation with cutting force and acoustic emission signals. Proc Inst Mech Eng Part B: J Eng Manuf 231(3):39–414. https://doi.org/10.1177/0954405415590562

  31. Songmene V, Khettabi R, Kouam J (2012) High Speed Machining: A Cost Effective & Green Process. Int J Manufacturing Research (IJMR) 7:229–256

    Article  Google Scholar 

  32. Phadke MS (1995) Quality engineering using robust design. Prentice Hall PTR

  33. Dornfeld D (1992) Acoustic emission feedback for precision deburring. CIRP Ann - Manuf Technol 41:93–96

    Article  Google Scholar 

  34. Mian AJ, Driver N, Mativenga PT (2011) Estimation of minimum chip thickness in micro-milling using acoustic emission. Proc Inst Mech Eng, Part B: J Eng Manuf 225:1535–1551

    Article  Google Scholar 

Download references

Availability of data and material

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Code availability

Not applicable

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohamad Javad Anahid & Seyed Ali Niknam

Authors
  1. Mohamad Javad Anahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Ali Niknam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The research results in this work were presented in Mr. Anahid's B.Sc thesis. Dr. Niknam also acted as an advisor on the work. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Seyed Ali Niknam.

Ethics declarations

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

All authors permit the publisher to publish the work.

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.

Highlights

•Milling experimental tests were performed on AA 7075 – T6 with TiCN, TiAlN and TiCN+Al2O3+TiN coating materials.

•Acoustic Emission (AE) signals were obtained from the milling tests.

•The Time-Frequency signal processing method was conducted on obtained AE signals.

•Feature extraction of signals and statistical analysis were adopted to determine the most sensitive AE parameters and governing machining factors.

•Advanced signal processing techniques and statistical analysis were utilized to determine sensitive AE parameters to changes in cutting parameters.

Appendix

Appendix

1.1 Description of AE parameters

Maximum value of the signal, Amplitude: AEMAX

(1)

Minimum value of the signal (Min) : AEMIN

(2)

Average value (mean) : \(A{E}_{\mu }=\frac{1}{n}\sum {x}_i\)

(3)

Root Mean Square (AERMS): AERMS is used to quantify the energy of signal:

\(A{E}_{RMS}=\sqrt{\frac{1}{n}\sum {x}_i}\)

(4)

Variance AEVAR:

\({\sigma}^2(VAR)=\frac{1}{n}\sum {\left({x}_i-\overline{x}\right)}^2\)

(5)

Standard deviation (σ):

\(\sigma =\sqrt{\frac{1}{n}\sum {\left({x}_i-\overline{x}\right)}^2}\)

(6)

Crest factor:

\({C}_F=\frac{X_{\textrm{max}}}{X_{rms}}\)

(7)

Form factor:

\({F}_F=\frac{M_1}{X_{rms}}\)

(8)

Coefficient of dispersion:

\({C}_D=\frac{\sigma }{X_{rms}}\)

(9)

Coefficient of asymmetry:

\({C}_A=\frac{s_B}{{\left({\sigma}^2\right)}^{\frac{3}{2}}}\)

(10)

Third-time statistical distribution (Skewness):

\({S}_B=\frac{1}{\sigma^3n}\sum {X}_i^3\)

(11)

Fourth-time statistical distribution (Kurtosis):

\({K}_B=\frac{1}{\sigma^4n}\sum {X}_i^4\)

(12)

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

Anahid, M.J., Niknam, S.A. Sensitivity of acoustic emission signals features to cutting parameters in time domain: case of milling aeronautical aluminium alloys. Int J Adv Manuf Technol 132, 265–275 (2024). https://doi.org/10.1007/s00170-024-13340-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13340-1

Keywords

Search

Navigation