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Performance evaluation of foam-filled honeycomb panels with reinforcements using experimental testing and machine learning

Publication Type : Journal Article

Publisher : SAGE Publications

Source : Journal of Composite Materials

Url : https://doi.org/10.1177/00219983251372686

Campus : Nagercoil

School : School of Engineering

Year : 2025

Abstract : The increasing demand for lightweight, cost-effective panels with high load-bearing capacity and minimal damage potential has driven advancements in sandwich composite materials, particularly for automotive applications. This study compares the performance of Stitch Reinforced Foam-filled Honeycomb Sandwich (SRFHS1), Pin Reinforced Foam-filled Honeycomb Sandwich (PRFHS1), and traditional Reinforced Foam-filled Honeycomb Sandwich (RFHS) panels. Experimental results revealed that the SRFHS1 panel achieved a peak load of 51,550 N in flatwise compression, 11,320 N in edgewise compression, and 1,520 N in flexural testing, outperforming PRFHS1 by 1.19, 1.19, and 1.33 times, and RFHS by 1.28, 1.26, and 2.03 times, respectively. Digital microscopy confirmed reduced damage in the SRFHS1 panel after flexural testing, highlighting superior durability. Machine learning analysis using ANOVA showed F-values of 11.497, 14.642, and 26.203 for flatwise, edgewise, and flexural tests, all exceeding critical thresholds with P -values <0.05, leading to the rejection of the null hypothesis and confirming significant differences among the populations for these parameters. Post-hoc Tukey HSD and box plot analysis identified SRFHS1 as the best-performing panel. These results establish SRFHS1 panels as ideal for transportation, offering durability, lightweight design, cost-effectiveness, and improved failure prediction and damage assessment capabilities.

Cite this Research Publication : E Dhanesh, VA Nagarajan, KP Vinod Kumar, RS Jayaram, Performance evaluation of foam-filled honeycomb panels with reinforcements using experimental testing and machine learning, Journal of Composite Materials, SAGE Publications, 2025, https://doi.org/10.1177/00219983251372686

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