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Machine learning optimisation and experimental evaluation of MWCNT-reinforced hybrid biocomposites for enhanced moisture resistance and energy absorption
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DOI：10.1080/01694243.2026.2691195.png)
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Abstract
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This research focused lightweight epoxy-based hybrid bio-composites reinforced with the alkaline-treated jute, bamboo, and flax fibres incorporating 0–6 wt.% multi-walled carbon nanotubes (MWCNTs) for improved mechanical properties, energy absorption, and moisture resistance for structural applications. The ten-layer laminates had symmetric laminate stackings (0°2J/0°2B/45°2F/0°2B/(0°2J), sequence were produced in compression molding. The MWCNTs were distributed into LY556-HY956 epoxy resin using mechanical stirring followed by ultrasonication process to get uniform distribution. Comprehensive mechanical testing under the standards of the tensile test, flexural test, interlaminar shear test, Izod impact test, punch shear test, and puncture test and also at a temperature of 25 °C to simulate test exposure conditions in engineering environments. These results showed a better performance at 4.5 wt.% MWCNTs, which resulted in the superior values of tensile and flexural strength (37.08 MPa and 44.9 MPa) and the enhanced interlaminar shear strength (ILSS) in comparison with the neat composites. These improvements were accompanied by much superior energy absorption in impact and puncture tests. SEM analysis revealed enhanced fiber–matrix interfacial bonding and effective crack bridging, resulting in improved mechanical integrity and damage tolerance. Moisture absorption was significantly reduced (least in 3–4.5 wt.%), which created tortuous diffusion channels that maintained the post-immersion integrity. The advanced machine learning framework produced highly accurate predictions with performance metrics of MSE: 0.0025, RMSE: 0.0503, MAE: 0.0503, and (R2 > 0.99), validation of optimal formulations. These sustainable composites serve to provide high-strength impact-resistant solutions for protective engineering applications that are low water absorption and sustainability for eco-efficient material design.
Keywords:
Machine learning
energy absorption
mechanical properties
sustainable
nanoparticle reinforcement
Journal
J
IF:
3.7
Papers:
340
Citations:
6.8K
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Cited Papers