Bending and Single Edge Notch Bending Test (SENB) Investigation of Hemp Fiber-Reinforced Epoxy Composites using Machine Learning

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    Santhosha M,

  • Lokesh K S,

  • Manjunatha G M,

  • Sandhya D S,

  • Venkatesh K C,

  1. Research Scholar, Department of Mechanical Engineering, Srinivas University Institute of Engineering and Technology, Srinivas University, Mangalore, Karnataka, India
  2. Professor, Department of Mechanical Engineering, Srinivas Institute of Technology, Srinivas University, Mangalore, Karnataka, India
  3. Research Scholar, Department of Mechanical Engineering, Srinivas University Institute of Engineering and Technology, Srinivas University, Mangalore, Karnataka, India
  4. Assistant Professor, Department of civil engineering, BGS Institute of Technology, Adichunchanagiri University, Karnataka, India
  5. Assistant Professor, Department of Mechanical Engineering Ballari Institute of Technology and Management, Ballari, Karnataka, India

Abstract

The present study aims to determine the behavior of hemp fiber-reinforced epoxy composites in terms of bending behavior and fracture toughness under bending load resembles the substitutive behavior of existing synthetic composites. The fabrication was carried out by hand lay-up assembly of hemp fiber with Lapox-12 epoxy resin volume fraction of 60:40 fiber: matrix volume. Flexural testing revealed an average strength of 93.5 ± 2.8 MPa and SENB testing revealed high notch sensitivity with ultimate stress of 181 ± 5.43 MPa and hence there is a need to ensure that the stress concentration is controlled. The XG-Boost model was created to forecast composite behavior and it proved to agree well with the experimental data, minimal residual errors (between 0.02) and bias in various load ranges. The residual analysis showed the error distribution to be close to normal as it indicated the viability of the model. These results, together with the sustainability advantages of renewability and minimized environmental footprint, justify the use of hemp-epoxy composites and the use of machine learning to minimize the experimental effort in material design. Present findings justify the usage of hemp-epoxy composites in work such as automotive interiors, building panels, and consumer products, and also demonstrate machine learning as a potent resource to limit the experimental effort to designing materials in the future.

Keywords: SENB test, Bending test, Hemp/epoxy composite, ML-modelling, Polynomial Regression.

How to cite this article:
Santhosha M, Lokesh K S, Manjunatha G M, Sandhya D S, Venkatesh K C. Bending and Single Edge Notch Bending Test (SENB) Investigation of Hemp Fiber-Reinforced Epoxy Composites using Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Santhosha M, Lokesh K S, Manjunatha G M, Sandhya D S, Venkatesh K C. Bending and Single Edge Notch Bending Test (SENB) Investigation of Hemp Fiber-Reinforced Epoxy Composites using Machine Learning. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243726


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Ahead of Print Subscription Original Research
Volume 14
03
Received 18/04/2026
Accepted 05/05/2026
Published 13/05/2026
Publication Time 25 Days
2

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