Skip to main content
SpringerLink
Log in

Vacuum bag leak detection for resin infusion: an electric current–based analogy

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

Abstract

The presence of leakages in composite manufacturing vacuum bag layups results in undesirable defects and low-quality products. Thus, it is crucial to locate leakages before injecting the resin. A reliable method is to estimate the location of the leakages by monitoring the flowrates of vacuum ports. However, these estimations based on traditional numerical methods often fail because of model inadequacies, extensive size of the vacuum bags, complex geometries and port configurations. Machine learning can be used for estimation, but it requires large datasets to capture the flow characteristics of every possible leakage location of various layup configurations. Generating such datasets in real manufacturing settings is prohibitively time-consuming and labor-intensive. We propose a novel analogy between vacuum bag assemblies and electrical circuits for flowrate data generation. This model has been experimentally validated for various geometries and patterns and acts as an accurate analogue for the real setup with an accuracy of more than 97%. Two machine learning–based models have also been trained with a validation accuracy of 94% that have a suitable prediction in regions far from the boundaries.

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

Similar content being viewed by others

Data availability

Data can be provided upon request.

References

  1. Goren A, Atas C (2008) Manufacturing of polymer matrix composites using vacuum assisted resin infusion molding. Arch Mater Sci Eng 34:117–120

    Google Scholar 

  2. Potter KD (1999) The early history of the resin transfer moulding process for aerospace applications. Compos A Appl Sci Manuf 30:619–621

    Article  Google Scholar 

  3. Centea T, Grunenfelder LK, Nutt SR (2015) A review of out-of-autoclave prepregs–material properties, process phenomena, and manufacturing considerations. Compos A Appl Sci Manuf 70:132–154

    Article  Google Scholar 

  4. Advani SG, Hsiao KT (eds) (2012) Manufacturing techniques for polymer matrix composites (PMCs). Elsevier

    Google Scholar 

  5. Campbell FC Jr (2003) Manufacturing processes for advanced composites. Elsevier

    Google Scholar 

  6. Hindersmann A (2019) Confusion about infusion: an overview of infusion processes. Compos A Appl Sci Manuf 126:105583

    Article  Google Scholar 

  7. Köber M, Eberth U (2007) Laminieren für Profis—Ein Trainingshandbuch für Faserverbundwerkstoffe, 2nd edn. Auflage, Airbus Deutschland GmbH, Hamburg

    Google Scholar 

  8. Brockmann R (2016) Ultra Sniffer–new leak detection method, 19th World conference on non-destructive testing 2016

  9. Haschenburger A, Menke N (2018) Sensor based analysis and identification of leakages in vacuum bagging for high performance composite components. In: International SAMPE technical conference series

  10. Lane MD, Poursartip A, Fernlund G, Floyd AM, Van Ee DA, Hibbert MEJ (2013) Detection, monitoring, and management of gas presence, gas flow and gas leaks in composites manufacturing, Canada Patent No. CA2817791A1

  11. Jens Bölke HU, Stefaniak D, Krombholz C (2014) Leakage detection, Germany Patent DE102011100096B4

  12. Advani SG, Zhou F, Alms JB, Corlay CC (2010) System and method of detecting air leakage in a VARTM process, US Patent No. US7762122B2

  13. Liu X, Li J, Zhu J, Wang Y, Qing X (2021) Cure monitoring and damage identification of CFRP using embedded piezoelectric sensors network. Ultrasonics 115:106470

    Article  Google Scholar 

  14. Miller FB, Benne ME (2013) Leak detection in vacuum bags. US Patent No. US8505361B2

  15. Haschenburger A, Heim C (2019) Two-stage leak detection in vacuum bags for the production of fibre-reinforced composite components. CEAS Aeronaut J 10:885–892

    Article  Google Scholar 

  16. Brill E, Lin JJ, Banko M, Dumais ST, Ng AY (2001) Data-intensive question answering. In: TREC, p 90, In Proceedings of the Tenth Text Retrieval Conference (TREC 2001), November 2001, Gaithersburg, Maryland

  17. Sun X, Li S, Lee LJ (1998) Mold filling analysis in vacuum-assisted resin transfer molding. Part I: SCRIMP based on a high-permeable medium. Polym Compos 19:807–817

    Article  Google Scholar 

  18. Kang M, Lee W, Hahn H (2001) Analysis of vacuum bag resin transfer molding process. Compos A Appl Sci Manuf 32:1553–1560

    Article  Google Scholar 

  19. Han K, Jiang S, Zhang C, Wang B (2000) Flow modeling and simulation of SCRIMP for composites manufacturing. Compos A Appl Sci Manuf 31:79–86

    Article  Google Scholar 

  20. Correia N, Robitaille F, Long A, Rudd C, Šimáček P, Advani SG (2004) Use of resin transfer molding simulation to predict flow, saturation, and compaction in the VARTM process. J Fluids Eng 126:210–215

    Article  Google Scholar 

  21. Nugroho G, Pranoto I, Rohmana NZ (2018) Effect of breather type and vacuum pressure on the manufacturing of an unmanned aerial vehicle fuselage using vacuum bagging method. In: AIP Conference Proceedings, p 040005

  22. Haschenburger A, Menke N, Stüve J (2021) Sensor based leakage detection in vacuum bagging, Int J Adv Manuf Technol 116:2413–2424

  23. Chen R, Dong C, Liang Z, Zhang C, Wang B (2004) Flow modeling and simulation for vacuum assisted resin transfer molding process with the equivalent permeability method. Polym Compos 25:146–164

    Article  Google Scholar 

  24. Convergent. COHO 102 single channel handheld leak detection. Catalogue. In: COHO, Convergent, Ed., ed, p 2, available at: www.convergent.ca

Download references

Funding

The authors would like to acknowledge the support of the Natural Sciences and Engineering Council Canada (NSERC) and Convergent Manufacturing Technologies Inc. program Grant PIDT GR017570 NSERC 2020 towards this research.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Victoria, 3800 Finnerty Rd., Victoria, Canada

    Yussuf Reza Esmaeili & Homayoun Najjaran

  2. School of Engineering, University of British Columbia, Okanagan Campus, 1137 Alumni Ave., British Columbia, Canada

    Brett Cosco & Homayoun Najjaran

Authors
  1. Yussuf Reza Esmaeili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brett Cosco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Homayoun Najjaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Homayoun Najjaran.

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

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

Esmaeili, Y.R., Cosco, B. & Najjaran, H. Vacuum bag leak detection for resin infusion: an electric current–based analogy. Int J Adv Manuf Technol 124, 1775–1786 (2023). https://doi.org/10.1007/s00170-022-10552-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10552-1

Keywords

Search

Navigation