Influence of curing pressure and surface treatment on mechanical properties of hybrid fiber metal laminates

M M. Megahed A A. M. Alsaeedy A A. E. Alshorbagy M M. Atta

Abstract

Abstract This study investigates the effect of key manufacturing parameters and graphene nanoparticle additions on the tensile behavior of fiber metal laminates (FMLs) using a Taguchi-based experimental design. Several manufacturing parameters were considered: laminate configuration (glass fiber and hybrid glass-carbon fiber reinforcement), aluminum surface treatment (chemical treatment and laser surface texturing with scanning spacings of 1 mm and 2 mm), aluminum thickness (0.5, 0.7, and 1.0 mm), graphene nanoparticle content (0, 0.1, and 0.25 wt%), and curing pressure (2, 5, and 7 bar). Eighteen FMLs specimens were fabricated according to the Taguchi orthogonal array and tested under tensile loading. Ultimate tensile strength ( $$\:{\upsigma\:}$$ ult ), tensile modulus ( E ), toughness modulus (U T ), and failure strain ( $$\:\epsilon\:$$ f ) were evaluated as performance responses. The findings show laminate configuration significantly affects $$\:{\upsigma\:}$$ ult , U T , and $$\:\epsilon\:$$ f , with the all-glass fiber configuration exhibiting superior performance responses. Graphene content and curing pressure had a minimal effect on tensile properties. The optimal parameter combination for $$\:{\upsigma\:}$$ ult and U T involved a glass fiber laminate configuration with chemically treated aluminum, an aluminum thickness of 0.5 mm, 0% graphene, and a curing pressure of 2 bar. Optimal parameters for E include a laser 1 mm scanning texture, glass fiber laminate configuration, aluminum thickness of 0.5 mm, 0% graphene nanoparticles, and a curing pressure of 5 bar. Additionally, optimal parameters for $$\:\epsilon\:$$ f are glass fiber configuration, chemical surface treatment, aluminum thickness of 1 mm, 0% graphene, and curing pressure of 2 bar. Validation tests indicated the model’s predictions were accurate, with prediction errors under 5%, highlighting its statistical reliability.

Article Details

Volume / Issue Vol. 16, Issue 1
Published July 17, 2026
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (4)

M

M. Megahed

A

A. M. Alsaeedy

A

A. E. Alshorbagy

M

M. Atta