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June 3, 2026Scientific Reports0 citationsOpen Access

Sustainable development and ANN-based prediction of bio-waste-filled flax–pineapple–epoxy hybrid composites for enhanced mechanical performance

SGSANDEEPKUMAR GOWDAMHMaruthi Prashanth B HRSRamesh S

Key Points

  • The study aims to evaluate the mechanical performance of bio-waste-filled hybrid composites for sustainable applications.
  • Incorporated four types of bio-waste fillers at 10 wt% into epoxy composites reinforced with flax and pineapple fibers.
  • Fabricated composites using hand layup and hot-pressing techniques.
  • Used an Artificial Neural Network to predict mechanical properties with high accuracy.
  • Coconut shell powder composites exhibited superior mechanical performance, with enhancements from 1.95% to 42.8% over other variants.
  • Mechanical properties including tensile strength, impact resistance, and interlaminar shear strength were significantly improved.
  • ANN predictions showed high accuracy: 95.85% for tensile, 83.9% for flexural, and 89.83% for impact properties.

Abstract

With the increasing demand for sustainable and cost-effective materials, natural fiber-reinforced composites are gaining traction among manufacturers and consumers. This study addresses the growing need for eco-friendly and structurally reliable composite materials. It focuses on the incorporation of bio-waste fillers into epoxy composites reinforced with flax and pineapple fibers, assessing their suitability for lightweight structural applications in automotive and construction sectors. Four fillers—coconut shell powder (CSP), teak wood dust (TWD), eggshell powder (ESP), and rice husk powder (RHP)—were added at a fixed 10 wt% to fabricate hybrid composites using a combination of hand layup and hot-pressing techniques. Mechanical properties such as tensile strength, impact resistance, interlaminar shear strength (ILSS), fracture toughness, and flexural strength were evaluated. Among all, CSP-reinforced composites showed superior mechanical performance, with enhancements ranging from 1.95% to 42.8% over other variants. Scanning Electron Microscopy (SEM) analysis revealed improved fiber–matrix bonding and minimal voids in CSP composites. In addition, an Artificial Neural Network (ANN) model was employed to predict mechanical properties with high accuracy: 95.85% (tensile), 83.9% (flexural), and 89.83% (impact). These results underscore the potential of using agricultural and industrial waste fillers in natural fiber composites for sustainable, high-performance applications in structural engineering.

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Cite This Study

GOWDA et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc56bdee9eb8c0dce6ccahttps://doi.org/10.1038/s41598-026-37015-x
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