Skip to main content

Advertisement

SpringerLink
Log in

Quantitative Damage Monitoring of Filament Wound Composites by Using Machine Learning-Based Techniques

  • Published:
Applied Composite Materials Aims and scope Submit manuscript

Abstract

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring technologies for composite tubes experiencing progressive damage were investigated. The challenges associated with quantitative failure monitoring were addressed, and the Genetic K-means algorithm, hierarchical clustering, and artificial neural network (ANN) methods were employed along with other three alternative methods. The impact characteristics and damage mechanisms of composite tubes under axial compressive load were assessed using Acoustic Emission (AE) monitoring and machine learning.Various failure modes such as matrix cracking, delamination, debonding, and fiber breakage were induced by layer bending. An increase in fibers/matrix separation and fiber breakage was observed with altered failure modes, while matrix cracking decreased Signal classification was achieved using hierarchical and K-means genetic clustering methods, providing insights into failure mode frequency ranges and corresponding amplitude ranges. The ANN model, trained with labeled data, demonstrated high accuracy in classifying data and identifying specific failure mechanisms. Comparative analysis revealed that the Random Forest model consistently outperformed the ANN and Support Vector Machine (SVM) models, exhibiting superior predictive accuracy and classification using ACC, MCC and F1-Score metrics. Moreover, our evaluation emphasized the Random Forest model's higher true positive rates and lower false positive rates. Overall, this study contributes to the understanding of model selection, performance assessment in machine learning, and failure detection in composite structures.

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

Similar content being viewed by others

Data Availability Statements

The authors can confirm that all data generated or analyzed during this study are included in the article and/or its supplementary information files.

References

  1. Thirumavalavan, K., Sarukasan, D.: Experimental investigation on multi-layered filament wound basalt/E-glass hybrid fiber composite tubes. Mater. Res. Express. 9(4), (2022)

  2. Bani Mohammad Ali, A., Alimirzaei, S., Ahmadi Najafabadi, M.: Evaluation of damage of filament wound composite tubes under lateral loading by acoustic emission method and finite element simulation. Modares. Mech. Eng. 22(11), 647–655 (2022)

    Article  Google Scholar 

  3. Mohamad, F., Hossein, H., Farzad, P., Ahmadi Najaf Abadi, M.: Composite materials damage characterization under quasi-static 3-point bending test using fuzzy C-means clustering. Appl. Mech. Mater. Trans. Tech. Publ. 1221–1228 (2012)

  4. Ameur, M.B., El Mahi, A., Rebiere, J.-L., Gimenez, I., Beyaoui, M., Abdennadher, M., Haddar, M.: Investigation and identification of damage mechanisms of unidirectional carbon/flax hybrid composites using acoustic emission. Eng. Fract. Mech. 216, 106511 (2019)

    Article  Google Scholar 

  5. Beheshtizadeh, N., Mostafapour, A., Davoodi, S.: Three point bending test of glass/epoxy composite health monitoring by acoustic emission. Alex. Eng. J. 58(2), 567–578 (2019)

    Article  Google Scholar 

  6. Jung, K.-C., Chang, S.-H.: Advanced deep learning model-based impact characterization method for composite laminates. Compos. Sci. Technol. 207, 108713 (2021)

    Article  CAS  Google Scholar 

  7. Azizian, M., Almeida, J.H.S., Jr.: Stochastic, probabilistic and reliability analyses of internally-pressurised filament wound composite tubes using artificial neural network metamodels. Mater. Today Commun. 31, 103627 (2022)

  8. Šofer, M., Cienciala, J., Fusek, M., Pavlíček, P., Moravec, R.: Damage analysis of composite CFRP tubes using acoustic emission monitoring and pattern recognition approach. Materials 14(4), 786 (2021)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  9. Park, D., Jung,  J., Gu, G.X., Ryu,  S.: A Generalizable and Interpretable Deep Learning Model to Improve the Prediction Accuracy of Strain Fields in Grid Composites. Mater. Des. 111192 (2022)

  10. Cui, R., Azuara, G., Lanza di Scalea, F., Barrera, E.: Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network. Struct. Health Monit. 21(3), 1123–1138 (2022)

    Article  Google Scholar 

  11. Kinga, D., Baadam, J.: A method for stochastic optimization, vol. 5, p. 6. International conference on learning representations (ICLR) (2015)

    Google Scholar 

  12. Seventekidis, P., Giagopoulos, D.: A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure. Mech. Syst. Signal Process. 157, 107735 (2021)

    Article  Google Scholar 

  13. Lyu, J., Akhavan, J., Manoochehri, S.: Image-based dataset of artifact surfaces fabricated by additive manufacturing with applications in machine learning. Data Brief 41, 107852 (2022)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Lyu, J., Akhavan Taheri Boroujeni, J., Manoochehri, S.: In-situ laser-based process monitoring and in-plane surface anomaly identification for additive manufacturing using point cloud and machine learning, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Ame. Soc. Mech. Eng. V002T02A030 (2021)

  15. Azimirad, V., Sotubadi, S.V., Nasirlou, A.: Vision-based Learning: a novel machine learning method based on convolutional neural networks and spiking neural networks, 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM). IEEE. 192–197 (2021)

  16. Zhao, Y., Noori, M., Altabey, W.A., Ghiasi, R., Wu, Z.: Deep learning-based damage, load and support identification for a composite pipeline by extracting modal macro strains from dynamic excitations. Appl. Sci. 8(12), 2564 (2018)

    Article  Google Scholar 

  17. Alimirzaei, S., Najafabadi, M.A., Nikbakht, A., Pahlavan, L.: Damage mechanism characterization of ±35° and ±55° FW composite tubes using acoustic emission method. 10567895221095603 (2022)

  18. Alimirzaei, S., Ahmadi Najafabadi, M., Nikbakht, A., Pahlavan, L.: Investigation of energy absorption capacity of 3D filament wound composite tubes: experimental evaluation, numerical simulation, and acoustic emission monitoring. Mech. Adv. Mater. Struct. 1–16 (2023)

  19. Saeedifar, M., Zarouchas, D.: Damage characterization of laminated composites using acoustic emission: A review. Compos. B Eng. 195, 108039 (2020)

    Article  CAS  Google Scholar 

  20. Yousefi, J., Najfabadi, M.A., Toudeshky, H.H., Akhlaghi, M.: Damage evaluation of laminated composite material using a new acoustic emission Lamb-based and finite element techniques. Appl. Compos. Mater. 25, 1021–1040 (2018)

    Article  ADS  Google Scholar 

  21. Fotouhi, M., Suwarta, P., Jalalvand, M., Czel, G., Wisnom, M.R.: Detection of fibre fracture and ply fragmentation in thin-ply UD carbon/glass hybrid laminates using acoustic emission. Compos. A Appl. Sci. Manuf. 86, 66–76 (2016)

    Article  CAS  Google Scholar 

  22. Kumar, C.S., Arumugam, V., Sajith, S., Dhakal, H.N., John, R.: Acoustic emission characterisation of failure modes in hemp/epoxy and glass/epoxy composite laminates. J. Nondestr. Eval. 34, 1–11 (2015)

    Article  Google Scholar 

  23. Woo, S.-C., Kim, T.-W.: High-strain-rate impact in Kevlar-woven composites and fracture analysis using acoustic emission. Compos. B Eng. 60, 125–136 (2014)

    Article  CAS  Google Scholar 

  24. Nimdum, P., Renard, J.: Use of acoustic emission to discriminate damage modes in carbon fibre reinforced epoxy laminate during tensile and buckling loading, ECCM 15–15th European Conference on Composite Mater. 8 (2012)

  25. Ni, Q.-Q., Iwamoto, M.: Wavelet transform of acoustic emission signals in failure of model composites. Eng. Fract. Mech. 69(6), 717–728 (2002)

    Article  Google Scholar 

  26. Saeedifar, M., Najafabadi, M.A., Zarouchas, D., Toudeshky, H.H., Jalalvand, M.: Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission. Compos. B Eng. 144, 206–219 (2018)

    Article  CAS  Google Scholar 

  27. Gutkin, R., Green, C., Vangrattanachai, S., Pinho, S., Robinson, P., Curtis, P.: On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses. Mech. Syst. Signal Process. 25(4), 1393–1407 (2011)

    Article  ADS  Google Scholar 

  28. Chou, H.-Y., Mouritz, A., Bannister, M., Bunsell, A.R.: Acoustic emission analysis of composite pressure vessels under constant and cyclic pressure. Compos. A Appl. Sci. Manuf. 70, 111–120 (2015)

    Article  CAS  Google Scholar 

  29. De Groot, P.J., Wijnen, P.A., Janssen, R.B.: Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites. Compos. Sci. Technol. 55(4), 405–412 (1995)

    Article  Google Scholar 

  30. Liu, P., Chu, J., Liu, Y., Zheng, J.: A study on the failure mechanisms of carbon fiber/epoxy composite laminates using acoustic emission. Mater. Des. 37, 228–235 (2012)

    Article  CAS  Google Scholar 

  31. Ceysson, O., Salvia, M., Vincent, L.: Damage mechanisms characterization of carbon fiber/epoxy composite laminates by both electrical resistance measurements and acoustic emission analysis. Scripta. Materialia. 34(8), (1996)

  32. Bourchak, M., Farrow, I., Bond, I., Rowland, C., Menan, F.: Acoustic emission energy as a fatigue damage parameter for CFRP composites. Int. J. Fatigue 29(3), 457–470 (2007)

    Article  CAS  Google Scholar 

  33. Morissette, L., Chartier, S.: The k-means clustering technique: General considerations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology 9(1), 15–24 (2013)

    Article  Google Scholar 

  34. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A k-means clustering algorithm. J. R. Stat. Soc. Ser. C Appl. Stat. 28(1), 100–108 (1979)

    Google Scholar 

  35. Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26(4), 354–359 (1983)

    Article  Google Scholar 

  36. Alimirzaei, S., Najafabadi, M.A., Ali, A.B.M.: Investigation of Failure Mechanism of the Composite Tubes Made by Filament Winding Process by Acoustic Emission Method. Amirkabir J. Mech. Eng. 54(6), 1357–1372 (2022)

    Google Scholar 

  37. Boominathan, R., Arumugam, V., Santulli, C., Sidharth, A.A.P., Sankar, R.A., Sridhar, B.: Acoustic emission characterization of the temperature effect on falling weight impact damage in carbon/epoxy laminates. Compos. B Eng. 56, 591–598 (2014)

    Article  CAS  Google Scholar 

  38. Mohammadi, M., Saidi, A.R., Jomehzadeh, E.: A novel analytical approach for the buckling analysis of moderately thick functionally graded rectangular plates with two simply-supported opposite edges. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 224(9), 1831–1841 (2010)

    Article  Google Scholar 

  39. Lee, K., Booth, D., Alam, P.: A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Syst. Appl. 29(1), 1–16 (2005)

    Article  Google Scholar 

Download references

Funding

This research was not supported by any specific grants from public, commercial, or not-for-profit funding agencies.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

    Amir Bani Mohammad Ali, Sajad Alimirzaei & Mehdi Ahmadi Najafabadi

  2. Department of Mechanical Engineering – Engineering Mechanics (MEEM), Michigan Technological University, Houghton, USA

    Saleh Valizadeh Sotubadi

  3. Department of Maritime and Transport Technology, TU Delft, Delft, The Netherlands

    Lotfollah Pahlavan

Authors
  1. Amir Bani Mohammad Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saleh Valizadeh Sotubadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sajad Alimirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Ahmadi Najafabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lotfollah Pahlavan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made significant contributions to the research presented in this manuscript. They have reviewed and approved the contents of the manuscript and have agreed to adhere to ethical standards.

Corresponding author

Correspondence to Sajad Alimirzaei.

Ethics declarations

Ethical Approval

This manuscript is an original work and has not been previously submitted, either partially or in full, for publication elsewhere. Furthermore, it has not been simultaneously submitted to multiple publications for consideration.

Conflict of Interest

The authors declared that they have no conflicts of interest in this work.

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

Bani Mohammad Ali, A., Valizadeh Sotubadi, S., Alimirzaei, S. et al. Quantitative Damage Monitoring of Filament Wound Composites by Using Machine Learning-Based Techniques. Appl Compos Mater 31, 223–247 (2024). https://doi.org/10.1007/s10443-023-10174-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10443-023-10174-0

Keywords

Search

Navigation