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Volume 13 | Issue 4 | Year 2026 | Article Id. IJME-V13I4P110 | DOI : https://doi.org/10.14445/23488360/IJME-V13I4P110

Acousto-Ultrasonic Assessment and Machine Learning-Based Defect Classification in Flax Fiber-Reinforced Composites and Natural Flax Fiber-Reinforced Polymer for Sustainable Structures

U Pranavi, K. T. Balaram Padal

Received Revised Accepted Published
20 Jan 2026 28 Feb 2026 27 Mar 2026 29 Apr 2026

Citation :

U Pranavi, K. T. Balaram Padal, "Acousto-Ultrasonic Assessment and Machine Learning-Based Defect Classification in Flax Fiber-Reinforced Composites and Natural Flax Fiber-Reinforced Polymer for Sustainable Structures," International Journal of Mechanical Engineering, vol. 13, no. 4, pp. 118-131, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I4P110

Abstract

This paper discusses the concept of Acousto-Ultrasonic Testing (AUT) as a reliable means of measuring the structural integrity of Natural Flax Fiber-Reinforced Polymer (NFRP) composites with non-destructive tests. The Stress Wave Factor was used as the main diagnostic parameter, in which AUT managed to identify and diagnose defects, including voids, delaminations, and microcracks, effectively. Compared to Carbon Fiber-Reinforced Polymer (CFRP) composites, NFRP laminates responded to high damping and variable responses due to the thickness and impact energy; thinner laminates were more effective at absorbing energy but were less stable when exposed to heavy loads. The results of the present study indicate that flax fibers can be used as environmentally friendly alternative materials in composite production. Machine Learning models, such as a Random Forest and Neural Network, have an ideal classification score (F1 score = 1.0), indicating a high degree of fault identification but necessitating more complex datasets to guarantee. This paper contributes to the effectiveness of AUT in sustainable composites and suggests the improvement of signal processing and real-time measurements of smart and environmentally friendly structural applications.

Keywords

Flax fiber-Reinforced Polymer (NFRP) composites, Acousto-Ultrasonic Testing (AUT), Machine Learning classification, Non-Destructive Evaluation (NDE), Fault identification and characterisation.

References

  1. Wei Qian et al., “Improving Non-destructive Testing Methods for Detecting Cavity Damage and Internal Defects in Stone Cultural Relics: A Focus on Ultrasonic Testing and Acoustic Tapping Technology,” Journal of Cultural Heritage, vol. 67, pp. 479-487, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. M. Seleznev, A. Weidner, and H. Biermann, “Detection of Early Fatigue Damage during Ultrasonic Fatigue Testing of Steel by Acoustic Emission Monitoring,” International Journal of Fatigue, vol. 185, pp. 1-12, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. G. Gautham Kishore Reddy et al., “Experimental Studies on Behavior of Hybrid Materials Concrete using Non-Destructive Testing (NDT) Methods,” Materials Today: Proceedings, pp. 1-6, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Xiaoyu Yang et al., “Comparative Study of Ultrasonic Techniques for Reconstructing the Multilayer Structure of Composites,” NDT & E International, vol. 121, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Muhammad Akhsin Muflikhun, and Bodo Fiedler, “Comprehensive Evaluation of CFRP Laminates using NDT Methods for Aircraft Applications,” Journal of Materials Research and Technology, vol. 32, pp. 395-409, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Ali Nokhbatolfoghahai, and Roger M. Groves, 6 - Integrity Assessment of Impacted Thick Composite Laminates using Phased Array Ultrasonic Testing, Non-destructive Testing of Impact Damage in Fiber-Reinforced Polymer Composites, Woodhead Publishing, pp. 151-185, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. James A. Quinn et al., “Novel Application of Ground Penetrating Radar for Damage Detection in thick FRP Composites,” Composites Part B: Engineering, vol. 284, pp. 1-15, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Andrzej Katunin et al., “Methodology of Residual Strength Prediction of Composite Structures with Low-velocity Impact Damage based on NDT Inspections and Numerical-experimental CAI Testing,” International Journal of Impact Engineering, vol. 181, pp. 1-21, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Saeid Hedayatrasa et al., “Vibro-Thermal Wave Radar: Application of Barker Coded Amplitude Modulation for Enhanced Low-Power Vibrothermographic Inspection of Composites,” Materials, vol. 14, no. 9, pp. 1-15, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Ozan Can Zehni et al., “Carbon Contrast Agents for Terahertz Spectroscopic NDT of Impacted Glass Fibre Reinforced Plastics,” Composites Science and Technology, vol. 257, pp. 1-9, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Martyna Strąg, and Waldemar Świderski, “Non-destructive Inspection of Military-designated Composite Materials with the use of Terahertz Imaging,” Composite Structures, vol. 306, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Omar S. Hassan et al., “Inspection of Antennas Embedded in Smart Composite Structures using Microwave NDT Methods and X-ray Computed Tomography,” Measurement, vol. 226, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Vishal Balasubramanian et al., “Non-destructive Erosive Wear Monitoring of Multi-layer Coatings using AI-enabled Differential Split Ring Resonator based System,” Nature Communications, vol. 14, pp. 1-12, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Shaun McKnight et al., “Three-Dimensional Residual Neural Architecture Search for Ultrasonic Defect Detection,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 71, no. 3, pp. 423-436, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Shaun McKnight et al., “A Comparison of Methods for Generating Synthetic Training Data for Domain Adaption of Deep Learning Models in Ultrasonic Non-Destructive Evaluation,” NDT & E International, vol. 141, pp. 1-14, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Neha Yadav et al., “In-line and Off-line NDT Defect Monitoring for Thermoplastic Automated tape Layup,” NDT & E International, vol. 137, pp. 1-8, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Meirbek Mussatayev et al., “Directional Eddy Current Probe Configuration for in-line Detection of Out-of-plane Wrinkles,” Composites Part B: Engineering, vol. 268, pp. 1-13, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Georgy Sunny, and T. Palani Rajan, “Review on Areca Nut Fiber and its Implementation in Sustainable Products Development,” Journal of Natural Fibers, vol. 19, no. 12, pp. 4747-4760, 2022.
  19. [CrossRef] [Google Scholar] [Publisher Link]
  20. Bo Madsen, and Hans Lilholt, “Physical and Mechanical Properties of Unidirectional Plant Fibre Composites—an Evaluation of the Influence of Porosity,” Composites Science and Technology, vol. 63, no. 9, pp. 1265-1272, 2003.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Libo Yan, Nawawi Chouw, and Krishnan Jayaraman, “Flax Fibre and its Composites – A Review,” Composites Part B: Engineering, vol. 56, pp. 296-317, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Jayamol George, M.S. Sreekala, and Sabu Thomas, “A Review on Interface Modification and Characterization of Natural Fiber Reinforced Plastic Composites,” Polymer Engineering & Science, vol. 41, no. 9, pp. 1471-1485, 2001.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Xue Li, Lope G. Tabil, and Satyanarayan Panigrahi, “Chemical Treatments of Natural Fiber for Use in Natural Fiber-Reinforced Composites: A Review,” Journal of Polymers and the Environment, vol. 15, pp. 25-33, 2007.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. M.M. Kabir et al., “Chemical Treatments on Plant-based Natural Fibre Reinforced Polymer Composites: An Overview,” Composites Part B: Engineering, vol. 43, no. 7, pp. 2883-2892, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. K.L. Pickering, M.G. Aruan Efendy, and T.M. Le, “A Review of Recent Developments in Natural Fibre Composites and their Mechanical Performance,” Composites Part A: Applied Science and Manufacturing, vol. 83, pp. 98-112, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. John Summerscales et al., “A Review of Bast Fibres and Their Composites. Part 1 – Fibres as Reinforcements,” Composites Part A: Applied Science and Manufacturing, vol. 41, no. 10, pp. 1329-1335, 2010.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Leonard Y. Mwaikambo, and Martin P. Ansell, “Chemical Modification of Hemp, Sisal, Jute, and Kapok Fibers by Alkalization,” Journal of Applied Polymer Science, vol. 84, no. 12, pp. 2222-2234, 2002.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Muhammad Muzammil Azad et al., “Intelligent Structural Health Monitoring of Composite Structures using Machine Learning, Deep Learning, and Transfer Learning: A Review,” Advanced Composite Materials, vol. 33, no. 2, pp. 162-188, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Xinlin Qing et al., “Machine Learning Based Quantitative Damage Monitoring of Composite Structure,” International Journal of Smart and Nano Materials, vol. 13, no. 2, pp. 167-202, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. Huanjia Hu, Xuebin Xu, and Cheng Liu, “Structural Health Monitoring of CFRP Composite Structures Using a Hybrid CNN-Vision Transformer Model,” 2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), Guangzhou, China, pp. 1-6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Kiran Kumar Amireddy, Krishnan Balasubramaniam, and Prabhu Rajagopal, “Deep Subwavelength Ultrasonic Imaging using Optimized Holey Structured Metamaterials,” Scientific Reports, vol. 7, pp. 1-8, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Arunkumar Anbalagan et al., “A Novel Microwave-based Non-destructive Testing System for Detection of Sub-surface Defects in Natural Flax Fiber Reinforced Composite Material,” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 238, no. 4, pp. 693-706, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Alireza Saberironaghi, Jing Ren, and Moustafa El-Gindy, “Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review,” Algorithms, vol. 16, no. 2, pp. 1-30, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. Pedro A. Ochôa, Roger M. Groves, and Rinze Benedictus, “Effects of High-amplitude Low-frequency Structural Vibrations and Machinery Sound Waves on Ultrasonic Guided Wave Propagation for Health Monitoring of Composite Aircraft Primary Structures,” Journal of Sound and Vibration, vol. 475, pp. 1-21, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  35. Mohamed Subair Syed Akbar Ali et al., “Characterization of Deep Sub-wavelength Sized Horizontal Cracks Using Holey-Structured Metamaterials,” Transactions of the Indian Institute of Metals, vol. 72, pp. 2917-2921, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]