Skip to main content
SpringerLink
Log in

Defect measurement in CFRP drilling based on digital image processing

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

Abstract

Drilling-induced burr and delamination are the main defects in carbon fiber–reinforced polymers (CFRPs). Although substantial CFRP defect measurement methods have been suggested, it is still challenging to recognize and measure the defects due to the drilling-induced chips, non-drilling-induced material reflections, and textured surface of CFRPs. To this end, the main purpose of the present paper is to develop a method able to recognize and measure the drilling-induced burr and delamination in CFRPs based on two techniques of bimodal threshold segmentation method and K-means clustering. To implement these techniques, images are captured by the image capturing setup with specific illumination. And the proposed defect measurement method is validated through full factorial drilling experiments in CFRP workpieces and compared with manual measurement supported by conventional image processing method. The results of the comparative measurement on the proposed method and manual method show that there is a reasonable agreement between them. Moreover, the proposed method can separate burr and delamination from regions of chips and textured surface of CFRPs. Considering the efficient, automated, and repeatable nature of the proposed method, it is anticipated to provide a meaningful reference for recognizing and measuring drilling-induced defects in the presence of strong interfering targets.

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

Similar content being viewed by others

Data availability

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

Code availability

Not applicable

References

  1. Forintos N, Czigany T (2019) Multifunctional application of carbon fiber reinforced polymer composites: electrical properties of the reinforcing carbon fibers – A short review. Composites, Part B 162:331–343. https://doi.org/10.1016/j.compositesb.2018.10.098

    Article  Google Scholar 

  2. Song Y, Cao H, Zheng W, Qu D, Liu L, Yan C (2022) Cutting force modeling of machining carbon fiber reinforced polymer (CFRP) composites: a review. Compos Struct 299:116096. https://doi.org/10.1016/j.compstruct.2022.116096

    Article  Google Scholar 

  3. Altin Karataş M, Gökkaya H (2018) A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Defence Technology 14:318–326. https://doi.org/10.1016/j.dt.2018.02.001

    Article  Google Scholar 

  4. Xu J, Yin Y, Paulo Davim J, Li L, Ji M, Geier N, Chen M (2022) A critical review addressing drilling-induced damage of CFRP composites. Compos Struct 294:115594. https://doi.org/10.1016/j.compstruct.2022.115594

    Article  Google Scholar 

  5. Cao S, Li HN, Tan G, Wu C, Huang W, Zhou Q, Hu Z (2023) Bi-directional drilling of CFRPs: from principle to delamination suppression. Composites, Part B 248:110385. https://doi.org/10.1016/j.compositesb.2022.110385

    Article  Google Scholar 

  6. Wang X, Wang F, Jin X, Fu R, Shi Y (2022) Numerical prediction of the chip formation and damage response in CFRP cutting with a novel strain rate based material model. Compos Struct 294:115746. https://doi.org/10.1016/j.compstruct.2022.115746

    Article  Google Scholar 

  7. Weng J, Saelzer J, Berger S, Zhuang K, Bagherzadeh A, Budak E, Biermann D (2023) Analytical and experimental investigations of rake face temperature considering temperature-dependent thermal properties. J Mater Process Technol 314:117905. https://doi.org/10.1016/j.jmatprotec.2023.117905

    Article  Google Scholar 

  8. Dong S, Liao W, Zheng K, Liu J, Feng J (2019) Investigation on exit burr in robotic rotary ultrasonic drilling of CFRP/aluminum stacks. Int J Mech Sci 151:868–876. https://doi.org/10.1016/j.ijmecsci.2018.12.039

    Article  Google Scholar 

  9. Fu Q, Wu S, Li C, Xu J, Wang D (2022) Delamination and chip breaking mechanism of orthogonal cutting CFRP/Ti6Al4V composite. J Manuf Processes 73:183–196. https://doi.org/10.1016/j.jmapro.2021.11.015

    Article  Google Scholar 

  10. Santos M, Santos J, Reis P, Amaro A (2021) Ultrasonic C-scan techniques for the evaluation of impact damage in CFRP. Mater Test 63:131–137. https://doi.org/10.1515/mt-2020-0020

    Article  Google Scholar 

  11. Vandendriessche J, Orta AH, Verboven E, Van Paepegem W, Van Den Abeele K, Kersemans M (2022) Probabilistic ultrasound C-scan imaging of barely visible impact damage in CFRP laminates. Compos Struct 284:115209. https://doi.org/10.1016/j.compstruct.2022.115209

    Article  Google Scholar 

  12. Gao Y, Hu W, Xin S, Sun L (2022) A review of applications of CT imaging on fiber reinforced composites. J Compos Mater 56:133–164. https://doi.org/10.1177/00219983211050705

    Article  Google Scholar 

  13. Yang X, Ju B-F, Kersemans M (2022) Assessment of the 3D ply-by-ply fiber structure in impacted CFRP by means of planar ultrasound computed tomography (pU-CT). Compos Struct 279:114745. https://doi.org/10.1016/j.compstruct.2021.114745

    Article  Google Scholar 

  14. Xu J, Li C, Mi S, An Q, Chen M (2018) Study of drilling-induced defects for CFRP composites using new criteria. Compos Struct 201:1076–1087. https://doi.org/10.1016/j.compstruct.2018.06.051

    Article  Google Scholar 

  15. Sugita N, Shu L, Kimura K, Arai G, Arai K (2019) Dedicated drill design for reduction in burr and delamination during the drilling of composite materials. CIRP Ann 68:89–92. https://doi.org/10.1016/j.cirp.2019.04.094

    Article  Google Scholar 

  16. Haeger A, Schoen G, Lissek F, Meinhard D, Kaufeld M, Schneider G, Schuhmacher S, Knoblauch V (2016) Non-destructive detection of drilling-induced delamination in CFRP and its effect on mechanical properties. Procedia Eng 149:130–142. https://doi.org/10.1016/j.proeng.2016.06.647

    Article  Google Scholar 

  17. Arns J-Y, Oromiehie E, Arns C, Prusty BG (2021) Micro-CT analysis of process-induced defects in composite laminates using AFP. Mater Manuf Processes 36:1561–1570. https://doi.org/10.1080/10426914.2020.1866192

    Article  Google Scholar 

  18. Khashaba UA (2022) A novel approach for characterization of delamination and burr areas in drilling FRP composites. Compos Struct 290:115534. https://doi.org/10.1016/j.compstruct.2022.115534

    Article  Google Scholar 

  19. Hrechuk A, Bushlya V, Ståhl J-E (2018) Hole-quality evaluation in drilling fiber-reinforced composites. Compos Struct 204:378–387. https://doi.org/10.1016/j.compstruct.2018.07.105

    Article  Google Scholar 

  20. Maghami A, Salehi M, Khoshdarregi M (2021) Automated vision-based inspection of drilled CFRP composites using multi-light imaging and deep learning. CIRP J Manuf Sci Technol 35:441–453. https://doi.org/10.1016/j.cirpj.2021.07.015

    Article  Google Scholar 

  21. Lukács T, Pereszlai C, Geier N (2023) Delamination measurement in glass fibre reinforced polymer (GFRP) composite based on image differencing. Composites, Part B 248:110381. https://doi.org/10.1016/j.compositesb.2022.110381

    Article  Google Scholar 

  22. Cui J, Liu W, Zhang Y, Han L, Yin P, Li Y, Zhou M, Wang P (2022) A visual inspection method for delamination extraction and quantification of carbon fiber reinforced plastic (CFRP). Measurement 196:111252. https://doi.org/10.1016/j.measurement.2022.111252

    Article  Google Scholar 

  23. Zhao YJ, Yan YH, Song KC, Li HN (2018) Robust and automatic measurement of grinding-induced subsurface damage in optical glass K9 based on digital image processing. Arch Civ Mech Eng 18:320–330. https://doi.org/10.1016/j.acme.2017.07.009

    Article  Google Scholar 

  24. Chen Z-l, Ye Y-t, Song Y-c, Luo Y, Liu L, Liu J-x (2013) Image correction method of color line-scan system. J. Opt. Photonics 03:318–321. https://doi.org/10.4236/opj.2013.32B074

    Article  Google Scholar 

  25. Lu Y, Young S, Wang H, Wijewardane N (2022) Robust plant segmentation of color images based on image contrast optimization. Comput Electron Agric 193:106711. https://doi.org/10.1016/j.compag.2022.106711

    Article  Google Scholar 

  26. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54:764–771. https://doi.org/10.1016/j.procs.2015.06.090

    Article  Google Scholar 

  27. Faraz A, Biermann D, Weinert K (2009) Cutting edge rounding: an innovative tool wear criterion in drilling CFRP composite laminates. Int J Mach Tools Manuf 49:1185–1196. https://doi.org/10.1016/j.ijmachtools.2009.08.002

    Article  Google Scholar 

  28. Voß R, Henerichs M, Rupp S, Kuster F, Wegener K (2016) Evaluation of bore exit quality for fibre reinforced plastics including delamination and uncut fibres. CIRP J Manuf Sci Technol 12:56–66. https://doi.org/10.1016/j.cirpj.2015.09.003

    Article  Google Scholar 

  29. Pereszlai C, Geier N (2020) Comparative analysis of wobble milling, helical milling and conventional drilling of CFRPs. Int J Adv Manuf Technol 106:3913–3930. https://doi.org/10.1007/s00170-019-04842-4

    Article  Google Scholar 

  30. Chen W-C (1997) Some experimental investigations in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates. Int J Mach Tools Manuf 37:1097–1108. https://doi.org/10.1016/S0890-6955(96)00095-8

    Article  Google Scholar 

  31. 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 115:1809–1820. https://doi.org/10.1007/s00170-021-07198-w

    Article  Google Scholar 

  32. Cao S, Li HN, Huang W, Zhou Q, Lei T, Wu C (2022) A delamination prediction model in ultrasonic vibration assisted drilling of CFRP composites. J Mater Process Technol 302:117480. https://doi.org/10.1016/j.jmatprotec.2021.117480

    Article  Google Scholar 

  33. Huang W, Zhang X, Wu C, Cao S, Zhou Q (2022) Tool wear prediction in ultrasonic vibration-assisted drilling of CFRP: a hybrid data-driven physics model-based framework. Tribol Int 174:107755. https://doi.org/10.1016/j.triboint.2022.107755

    Article  Google Scholar 

Download references

Funding

The work described in this paper is supported by the National Natural Science Foundation of China (Grant No: 52275506).

Author information

Authors and Affiliations

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

    Xuyan Zhang, Wenjian Huang, Chaoqun Wu & Shiyu Cao

  2. Hubei Province Engineering Research Center of Robot & Intelligent Manufacturing, Wuhan, 430070, China

    Chaoqun Wu

Authors
  1. Xuyan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaoqun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiyu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xuyan Zhang: writing—original draft preparation, methodology. Wenjian Huang: investigation, validation, reviewing and editing. Shiyu Cao: investigation, reviewing and editing. 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.

Xuyan Zhang and Wenjian Huang equally contributed to this work.

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

Zhang, X., Huang, W., Wu, C. et al. Defect measurement in CFRP drilling based on digital image processing. Int J Adv Manuf Technol 127, 5405–5419 (2023). https://doi.org/10.1007/s00170-023-11838-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11838-8

Keywords

Search

Navigation