Numerical identification of a new Least Attenuated Wave (LAW) mode for high-resolution ultrasonic inspection of viscoelastic composite laminates.

Experimental and numerical study on delamination in tapered composite laminates under tension–torsion loading

Thermo-mechanical performance evaluation of hybrid NiTi/CF-PEKK composite laminates using experiments and machine learning approaches

Impact localization in composite laminates by minimally intrusive piezoelectric nanostructured sensor

The mechanical performance of fibre-reinforced polymer (FRP) is strongly temperature dependent and governed by glass transition, pyrolysis of the polymer matrix and, in the case of carbon fibres, oxidation of the fibres. To understand the mechanical response, it is desirable to develop simplified numerical tools capable of reproducing the temperatures inside a FRP when it is exposed to a fire. A simplified one-dimensional numerical model was developed incorporating through-thickness heat conduction, boundary heat exchange, and temperature-dependent material decomposition. It uses constant thermal properties to enable rapid computational predictions while maintaining reasonable accuracy for thermomechanical applications. Carbon and glass FRP composite laminates with laid-up thermocouples were exposed to controlled radiant heat fluxes of 10, 30, and 50 kW/m 2 , enabling precise, repeatable through-thickness temperature measurements. Experimental results reveal that glass transition temperature progression occurs in depth significantly early relative to in depth pyrolysis, indicating mechanical failure and loss of structural capacity likely precedes thermal decomposition. Model validation showed that accurate material property characterisation is more critical than including complex pyrolysis chemistry for predicting glass transition progression. The framework serves as a valuable intermediate tool between analytical estimates and finite element simulations, offering practical utility for thermomechanical failure prediction. • Carbon- and glass-fibre reinforced polymer materials tested in cone calorimeter • Precisely laid-up thermocouples provide high fidelity temperature measurement • Simplified 1-D model compares reasonably well to experimental results • Complex reaction modelling is unnecessary for thermomechanical analyses

Entropy-based modeling of fatigue in angle-ply and quasi-isotropic composite laminates

This work examined the role of alkaline and epoxy-based surface chemical treatments on the mechanical and thermal properties of flax fiber reinforced Aluminum Alloy 6082 hybrid Fiber Metal Laminates (FMLs), integrated with machine learning (ML) predictive frameworks. Flax fiber mats underwent surface modification via a 1% NaOH alkaline soak followed by an epoxy sizing treatment to promote stronger bonding with the aluminum matrix. Concurrently, aluminum sheets were treated with an NaOH-Na₂CO₃ alkaline bath to enhance metal-polymer interfacial compatibility. Composite laminates were manufactured through hand layup combined with compression molding at 30bar and 70°C curing for four hours. Tensile characterization followed ASTM D638 protocols, thermal conductivity measurements employed a guarded hot plate (GHP) system per ASTM C177, and corrosion evaluation relied on gravimetric immersion in 3.5wt.% NaCl over 30days. Predictive models using Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF) were developed on a structured 180-observation experimental dataset. Steady-state thermal conductivity values of 0.1039W/m·K and 0.065W/m·K were recorded for untreated and chemically treated FFAL laminates, respectively. Surface treatment yielded a 31% gain in tensile strength (168.5MPa to 220.7MPa) and a 34% rise in tensile modulus (12.3 GPa to 16.34 GPa), while thermal conductivity declined by 37.4%. Among all models, the Random Forest algorithm demonstrated superior predictive capability with R2 = 0.972 for tensile strength and R2 = 0.969 for thermal conductivity; MAPE values remained below 3.5%. All experimental trials were performed in triplicate with mean values reported alongside standard deviation. These findings substantiate that combining chemical surface modification with data-driven ML optimization constitutes a reliable design methodology for high-performance, sustainable natural fiber metal composites.

HighlightsThe standard VCCT results fall between the revised VCCT results in ADCB and AENF.Contact between the specimen arms can lead to unrealistic negative VCCT values.In the revised VCCTs, ERR transfer occurs between modes I and II.The objective of this article is to compare the standard two-step virtual crack-closure technique (VCCT) and the revised I–II and II–I VCCT developed by Valvo by studying two asymmetric test configurations commonly used to produce mixed-mode delamination in composite laminates—the asymmetric double cantilever beam (ADCB) and asymmetric end-notched flexure (AENF) configurations—via finite element modelling (FEM). Scientific literature has revealed that highly asymmetric specimens may exhibit negative components of the energy release rate (ERR) under certain specific loading conditions when using the standard VCCT. The revised VCCTs establish an alternative ERR partition with energetically orthogonal components to solve this inconsistency. This study aims to better understand the mechanisms involved in the revised VCCTs. This study demonstrates that, when using the revised methods, there is a transfer of energy between modes I and II, unlike when using the standard VCCT. The values of the mode I and mode II components of the ERR produced by the standard VCCT fall between the values produced by the revised I–II and II–I VCCTs for the test configurations. Nevertheless, as expected, the total ERR calculated using the three procedures is the same. Finally, some considerations are drawn for the scenario when contact occurs between the specimen arms in the AENF configuration, as it can also lead to unrealistic negative mode I ERR values in the FEM analysis.

This paper presents a novel, efficient computational framework for the optimisation of the fundamental frequency of multi-layered composite slabs with consideration of uncertainties. The approach is based on Finite Element Method (FEM) data generation, Deep Neural Network (DNN) surrogate modelling, deterministic optimisation using the genetic algorithm (GA), Morris Sensitivity Analysis (SA), and quantile-based optimisation, including uncertainties and using the GA. Different boundary condition configurations are considered. The surrogate model is trained on FEM-generated samples and subsequently used to replace expensive modal analyses during optimisation, significantly reducing the optimisation evaluation cost for one boundary condition variant. The proposed method achieves near-identical optimal non-dimensional parameter values to those reported in the literature for Bayesian Optimisation (BO), with discrepancies of less than 0.5%. To improve robustness to manufacturing tolerances, an additional uncertainty-aware optimisation is performed, in which model parameters are perturbed with normally distributed noise. By maximising the 5% quantile of the non-dimensional parameter , robust optimal solutions are obtained with minimal loss in performance. Overall, the DNN-GA framework enables fast and accurate optimisation of composite laminates and provides both deterministic and robust design recommendations at a fraction of the computational cost of traditional FEM-based optimisation workflows.

Curing deformation prediction of aircraft-grade toughened composites based on the indirect characterization method

Auxetic dimensionality governs impact resilience in composite laminates

Size effect of fiber veils on the interlaminar toughening performance and underlying mechanism in composite laminates

Adaptive sampling for efficient Lamb wavefield reconstruction in composite laminates with Spatial-Temporal Masked AutoEncoder.

Design and performance analysis of inner-rotor IPM PMSMs for cogging torque and ripple reduction in EV applications

Impact damage tolerance and compression after impact failure mechanism of regularly arrayed short fiber-reinforced composite laminates

A novel non-linear orthotropic constitutive model with deformation induced fiber reorientation effect for composite laminate simulation

Fracture analysis of basalt fiber-reinforced composite laminates: Experimental study and analytical model

A review of ballistic impact behavior and multiscale simulation of composite laminates in wind turbine blades

Thermally induced multistability in cured shapes of hybrid composite laminates

Lagrange multiplier and penalty methods for the thermal buckling analysis of higher-order composite laminates