Hygrothermal durability of Elium® glass–carbon hybrid laminates: role of stacking architecture and data-driven sensitivity analysis

Ulus, Hasan and Manav, Ahmet Selçuk and Kaybal, Halil Burak (2026) Hygrothermal durability of Elium® glass–carbon hybrid laminates: role of stacking architecture and data-driven sensitivity analysis. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications . ISSN 1464-4207 (Print) 2041-3076 (Online) Published Online First https://dx.doi.org/10.1177/14644207261447288

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Abstract

Hybrid glass–carbon laminates are attractive for marine and humid-service structures, but their durability in hot–wet environments is strongly controlled by stacking architecture. This study investigates the flexural and thermomechanical response of Elium®-matrix laminates with five lay-up configurations, namely fully glass (GF), fully carbon (CF), block hybrid (BH), symmetric hybrid (SH), and functionally graded hybrid (FGH), after hygrothermal aging. Panels were immersed in deionized water at 55 °C until near saturation and their hygrothermal durability was evaluated by water uptake measurements, flexural testing, and dynamic mechanical analysis (DMA). CF exhibits the highest initial stiffness and strength but fails in a brittle manner, whereas the graded FGH lay-up offers the best strength–toughness balance. Hygrothermal conditioning reduces stiffness and strength in all laminates, however, CF retains 85–90% of its flexural properties and FGH consistently shows the highest retention among the hybrid designs. A hygrothermal performance index (HPI) is proposed by integrating flexural strength, flexural modulus, and storage-modulus retention with normalized equilibrium moisture uptake, and a random forest-based analysis is employed to rank the relative importance of lay-up configuration, moisture-related variables, and DMA descriptors. The glass fraction on the compressive side, the number of carbon–glass interfaces and the storage-modulus retention appear to be the dominant variables within the limited dataset, providing preliminary design insight for Elium®-based hybrids in hot–wet conditions.
Item Type: Article
Uncontrolled Keywords: Functionally graded stacking; hygrothermal aging; machine-learning (ML) based sensitivity analysis; mechanical properties; thermoplastic Elium®matrix
Divisions: Integrated Manufacturing Technologies Research and Application Center
Depositing User: Hasan Ulus
Date Deposited: 21 May 2026 15:47
Last Modified: 21 May 2026 15:47
URI: https://research.sabanciuniv.edu/id/eprint/54089

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