The value of the stress intensity factor (SIF) range thresholdΔKthfor fatigue crack growth (FCG) depends highly on its experimental identification. The identification and application ofΔKthare not well established as its determination depends on various factors including experimental, numerical, or analytical techniques used. A new analytical model which can fit the raw FCG experimental data is proposed. The analytical model proposed is suitable to fit with high accuracy the experimental data and is capable of estimating the threshold SIF range. The comparison between the threshold SIF range identified with the model proposed and those found in the literature is also discussed.ΔKthidentified is found to be quite accurate and consistent when compared to the literature with a maximum deviation of 5.61%. The accuracy with which the analytical model is able to fit the raw data is also briefly discussed.

1. 1.1. Unmanned Aerial Vehicles (UAVs), particularly quadrotor-type minidrones, have become important experimental systems for advancing autonomous flight, control engineering, embedded systems im...

Fatigue crack growth depends heavily on near tip stress-strain behavior controlled by many micromechanical and microstructural factors. Crack closure is widely used to rationalize crack growth behaviour under complex loading conditions. Reliable crack closure measurement is essential for enhanced damage tolerance design and remains a challenge to the industry. This paper focuses on the effect of plastic deformation ahead of a notch/crack on the non-linearity of compliance curves of 6082-T651 aluminium alloy specimens to highlight a potential issue in the conventional compliance curve based crack closure measurement technique. Experimental and numerical simulation results demonstrate that plastic deformation ahead of the notch will introduce non-linear stress-strain behavior in the absence of crack closure. It is proposed that the effect of crack tip plasticity on the non-linearity of the compliance curve be separated to obtain reliable crack closure measurement.

ARotor swept area (m2)BEMBlade Element Momentum methodCmMoment coefficient =M0.5ρU2ARCBlade chord length (m)DRotor diameter (m)DOEDesign of ExperimentsFAFixing Angle (°), preset angular offset of b...

This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang–Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.