Production Engineering

, Volume 13, Issue 5, pp 617–626 | Cite as

Artificial intelligence for non-destructive testing of CFRP prepreg materials

  • Carsten Schmidt
  • Tristan HockeEmail author
  • Berend Denkena
Quality Assurance


This paper presents a concept of the quality assurance for CFRP prepreg materials and focusses on the classification of thermographic images using convolution neural networks (CNNs). The method for non-destructive testing of CFRP prepreg materials combines a laser-triangulation sensor and an infrared camera to monitor both, the geometry and the impregnation of the prepreg material. The aim is to ensure a high material quality excluding any defective material in an early stage of the process chain of the production of CFRP components. As a result, the reliability of Automated-Fiber-Placement processes utilizing this previously tested material increases. Therefore, an artificial intelligence is set up to classify the thermal images of the CFRP material. Two different architectures of CNN are trained and validated with data sets consisting of thermal images of several prepreg materials and different material defects, such as geometric deviations and varying fiber-matrix-ratios caused by an incorrect impregnation. The CNNs are able to differentiate prepreg materials and to detect and classify certain material-independent defects for known and trained prepreg materials.


Artificial Intelligence Automated-Fiber-Placement Defects Prepreg Quality assurance 



The authors would like to thank the Federal state of Lower Saxony and the Volkswagen Foundation for funding the research project “Multi-Matrix-Prepreg”. For further information, visit the website (Grand No. ZN3063).


  1. 1.
    Rudberg T, Neilson J, Henschied M, Cemenska J (2014) Improving AFP cell performance. In: SAE International Journal of Aerospace Manufacturing and Automated Fastening ConferenceGoogle Scholar
  2. 2.
    Alexandra K, Linb S, Brabandta D, Böhlkeb T, Lanzaa, G (2014) Quality control in the production process of SMC lightweight material. In: Proceedings of the 47th CIRP conference on manufacturing, 47: 772–777,
  3. 3.
    Lukaszewicz D (2011) Optimization of high-speed automated layup of thermoset carbon fiber preimpregnates. Dissertation, University of BristolGoogle Scholar
  4. 4.
    Schulz M, Goldbach S, Heuer H, Meyendorf N (2011) Ein Methodenvergleich—ZfP an Kohlefaserverbundwerkstoffen mittels wirbelstrom- und ultraschallbasierender Prüfverfahren. DGZfP—JahrestagungGoogle Scholar
  5. 5.
    Denkena B, Schmidt C, Völtzer K, Hocke T (2016) Thermographic online monitoring system for Automated Fiber Placement processes. Compos Part B 97:239–243. CrossRefGoogle Scholar
  6. 6.
    Dattoma V, Panella FW, Pirinu A, Saponaro A (2019) Advanced NDT methods and data processing on industrial CFRP components. Appl Sci. Google Scholar
  7. 7.
    Fleischer J et al (2018) Composite materials parts manufacturing. CIRP Ann Manuf Technol 67:603–626. CrossRefGoogle Scholar
  8. 8.
    Zhang H et al (2018) A novel optical air-coupled ultrasound NDE sensing technique compared with infrared thermographic NDT on impacted composite materials. In: proc. SPIE 10661, thermosense: thermal infrared applications XL, 106610X,
  9. 9.
    Schumacher D, Meyendorf N, Hakim I, Ewert U (2018) Defect recognition in CFRP components using various NDT methods within a smart manufacturing process. In: AIP conference proceedings. 44th annual review of progress in quantitative nondestructive evaluation, Vol. 37,
  10. 10.
    Chang Y-A, Yan Z, Wang K-H, Yao Y (2016) Non-destructive testing of CFRP using pulsed thermography and multi-dimensional ensemble empirical mode decomposition. J Taiwan Inst Chem Eng 61:54–63CrossRefGoogle Scholar
  11. 11.
    Caggiano A, Nele L (2018) Comparison of drilled hole quality evaluation in CFRP/CFRP stacks using optical and ultrasonic non-destructive inspection. Mach Sci Technol 22:865–880. CrossRefGoogle Scholar
  12. 12.
    Heuer H et al (2015) Review on quality assurance along the CFRP value chain—Non-destructive testing of fabrics, preforms and CFRP by HF radio wave techniques. Compos Part B Eng 77:494–501. CrossRefGoogle Scholar
  13. 13.
    Gäbler S, Heuer H, Heinrich G, Kupke R (2015) Quantitatively analyzing dielectrical properties of resins and mapping permittivity variations in CFRP with high-frequency eddy current device technology. AIP Conf Proc 1650:336–344CrossRefGoogle Scholar
  14. 14.
    Schmidt C, Denkena D, Hocke T, Völtze K (2017) Thermal imaging as a solution for reliable monitoring AFP processes. In: 3rd international symposium on automated composites manufacturing (ACM)Google Scholar
  15. 15.
    Weimer D, Scholz-Reiter B, Shpitalni M (2016) Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. In: CIRP Annals—Manufacturing Technology, Vol. 65, pp. 417–420.
  16. 16.
    Marani R, Palumbo D, Galietti U, Stella E, D’Orazio T (2016) Automatic detection of subsurface defects in composite materials using thermograpgy and unsurpervised machine learning. In: 8th IS.
  17. 17.
    He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: IEEE conference on computer vision and pattern recognition, pp. 770–778.

Copyright information

© German Academic Society for Production Engineering (WGP) 2019

Authors and Affiliations

  • Carsten Schmidt
    • 1
  • Tristan Hocke
    • 1
    Email author
  • Berend Denkena
    • 1
  1. 1.Institute of Production Engineering and Machine Tools, Leibniz Universität HannoverStadeGermany

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