Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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Sustainable Hemp-Based Composites: Temperature-Dependent Tensile Prediction via Machine Learning and FEM

Authors
Madicharla Adithya1, Nandamuri Charith Jaya Sai1, Penti Bhuvan Kumar1, Somisetty Yogendra1, Taraka Rupa Sri Nitya Chowdary Movva2, Phani Prasanthi3, *
1UG students, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
2Postgraduate student, Applied Analytics department, Columbia University, New York, USA
3Professor, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
*Corresponding author. Email: phaniprasanthi.parvathaneni@gmail.com
Corresponding Author
Phani Prasanthi
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_39How to use a DOI?
Keywords
Tensile strength; Simulation studies; Machine learning models
Abstract

This study addresses sustainable hemp fiber/epoxy composites with and without fillers—carbon powder (Cp) and groundnut shell powder (GNSP)—as eco-friendly alternatives to synthetic materials for high-temperature applications. According to the experimental results, hemp + GNSP + epoxy had the maximum tensile strength (41.38 MPa), followed by hemp + Cp + epoxy (40.12 MPa) and hemp + epoxy (33.83 MPa). ANSYS simulations (40–80 ℃) were conducted using material properties obtained from stress–strain data, and predictive analysis was conducted using machine learning algorithms. According to simulations and experimental studies, the Support Vector Regression (SVR) model outperformed Random Forest and Linear Regression in terms of accuracy. The results also demonstrate that GNSP-reinforced hemp composites showed improved tensile strength retention compared to plain hemp–epoxy and CP-reinforced composites.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_39How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Madicharla Adithya
AU  - Nandamuri Charith Jaya Sai
AU  - Penti Bhuvan Kumar
AU  - Somisetty Yogendra
AU  - Taraka Rupa Sri Nitya Chowdary Movva
AU  - Phani Prasanthi
PY  - 2025
DA  - 2025/12/31
TI  - Sustainable Hemp-Based Composites: Temperature-Dependent Tensile Prediction via Machine Learning and FEM
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 535
EP  - 546
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_39
DO  - 10.2991/978-94-6463-940-7_39
ID  - Adithya2025
ER  -