Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Suresh Poyil Subramanyam; Dilip Kumar Kotikula; Basavaraju Bennehalli; Atul Babbar; Sagr Alamri; Alaauldeen A Duhduh; Ali A Rajhi; Raman Kumar; Ketan Kotecha

doi:10.1021/acsomega.3c08164

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

ACS Omega. 2024 Feb 5;9(7):8019-8036. doi: 10.1021/acsomega.3c08164. eCollection 2024 Feb 20.

Authors

Suresh Poyil Subramanyam¹, Dilip Kumar Kotikula², Basavaraju Bennehalli³, Atul Babbar⁴, Sagr Alamri⁵, Alaauldeen A Duhduh⁶, Ali A Rajhi⁵, Raman Kumar⁷, Ketan Kotecha⁸

Affiliations

¹ Department of Mechanical Engineering, Alva's Institute of Engineering and Technology, Mijar, Karnataka 574225, India.
² Department of Mechanical Engineering, K S Institute of Technology, Bengaluru, Karnataka 560109, India.
³ Department of Chemistry, MVJ College of Engineering, Bengaluru, Karnataka 560067, India.
⁴ Department of Mechanical Engineering, SGT University, Gurugram, Haryana 122505, India.
⁵ Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
⁶ Department of Mechanical Engineering Technology, CAIT, Jazan University, Prince Mohammed Street, PO Box 114, Jazan 45142, Saudi Arabia.
⁷ Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab 141006, India.
⁸ Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International University Pune, Pune 411014, India.

Abstract

Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in the building, automotive, and packing industries. The novelty of the work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses on three-body abrasion wear testing machines along with its mechanical features. The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a machine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) and the coefficient of friction (COF). The void fraction is observed to vary between 0.261 and 3.8% as the fiber fraction is incremented. The hardness of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent in flexural strength and its modulus is seen for 0 to 12 wt % composite specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen. A progressive rise in the ILSS and impact strength is perceptible. The wear behavior of the specimens is reported. The worn surface morphology is studied to understand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high R-squared values coupled with low mean-square error and mean absolute error metrics. Rigorous statistical validation employing paired t tests confirmed the model's suitability, revealing no significant disparities between predicted and actual values for both the SWR and COF.