Damped AC voltage (DAC) has been widely used for on-site partial discharge detection of XLPE power cables, whereas the effect of its DC charging stage on cable insulation remains unclear. In this work, XLPE specimens were subjected to repeated DAC voltage applications, and the resulting physicochemical properties, trap characteristics and space-charge behavior were investigated by Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermally stimulated depolarization current (TSDC), DC conductivity and pulsed electroacoustic (PEA) measurements. The results show that no obvious chemical change or crystallinity variation occurs after 500 DAC applications. However, at this stage, the trap density increases from 3.51×1015 m-3 to 1.06 × 1016 m-3 (by approximately three times), with space charge evolving from interfacial accumulation to full penetration across the sample, and the electric field distortion ratio rising from 19.3% to 47.6%. These findings suggest a possible transition from interface-dominated to synergistic interface–bulk charge interaction in XLPE under repetitive DAC stress, which may represent an important stage in the electrical aging process of this material.
To examine the influence of key process parameters in fused deposition modelling on the tensile behaviour of Polylactic Acid (PLA) fabricated via three-dimensional printing. The influence of process parameters such as Raster Angle, Layer thickness, and Infill density was investigated to identify the best process combinations that enhance tensile behaviour, and machine learning (ML) models were employed to identify the most accurate predictive model for tensile strength and to establish an integrated framework for process optimisation. A total of 27 samples were fabricated using Fused Deposition Modelling with varying layer thicknesses (0.18, 0.22, and 0.25 mm) and infill densities (25%, 50%, and 75%). Tensile testing was conducted in accordance with ASTM D638 standards, followed by an analysis of variance (ANOVA) to quantify the significance of each process parameter. Five machine learning models, namely Decision Tree, Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regression, were developed and evaluated to predict the tensile strength. The tensile strength of the fabricated specimens ranged from 23.91 MPa to 28.798 MPa. The highest tensile strength was achieved with a layer thickness of 0.18 mm, infill density of 75%, and a (45°, 90°) raster angle. ANOVA statistical analysis revealed that layer thickness was the most influential parameter on tensile strength, followed by infill density and raster angle in order of significance. Among the five ML models evaluated, the Decision Tree model demonstrated the highest predictive accuracy (Mean R² = 0.8577), outperforming the Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regression. This study offers a comprehensive assessment of the effects of key FDM process parameters by integrating tensile testing, ANOVA, and machine learning within a unified framework. The proposed methodology not only identifies the optimum process parameter combination but also determines the most suitable predictive model for tensile strength, providing an effective framework for process optimisation and reducing the experimental effort in additive manufacturing.
This study investigates the mechanistic behaviour of estrone (E1) adsorption onto chitosan hydrogel beads through the assessment of adsorption kinetics, equilibrium isotherms, and adsorbent regeneration. Batch adsorption experiments were conducted to investigate the influence of pH, contact time, initial (E1) concentration, adsorbent load, and agitation speed on E1 uptake. Classical kinetic models, viz., pseudo-first order (PFO), pseudo-second order, intraparticle diffusion models and fractal-like kinetic models were applied to elucidate rate-controlling mechanisms. The PFO model best statistical fit (i.e., R2 = 0.9939, adj.R2 = 0.9918, χ^2 = 5.25E-4) with calculated qe1 = 4.8891 mg.g-1, and qexp = 4.95 mg.g-1 indicating film-diffusion dominated physisorption. Although fractal-like kinetics captured heterogeneous and anomalous diffusion associated with the hydrogel’s irregular structure, they did not outperform the classical PFO model. Equilibrium data were fitted to Langmuir, Freundlich, Langmuir-Freundlich, and Hill isotherms. The Langmuir model gave the best statistical performance (i.e., R2 = 0.9170, adj.R2 = 0.8754, QL = 6.7766 mg.g-1, and KL = 1.0277 L.mg-1) suggesting predominant monolayer adsorption, whereas the other models demonstrated underlying surface heterogeneity and mild cooperative effects. Regeneration studies demonstrated that the hydrogels retained a substantial portion of their adsorption capacity over eight cycles, confirming structural stability and practical reusability.
Carbon fiber-reinforced polyethylene terephthalate glycol (PETG-CF) has emerged as a promising material for lightweight structural applications owing to its favorable mechanical properties and printability. However, the combined influence of fused filament fabrication (FFF) process parameters on its tensile performance and material efficiency remains inadequately understood. This study experimentally investigated the effects of infill shape, print speed, layer height, infill density, and raster angle on the tensile strength and specific strength of PETG-CF using a Taguchi orthogonal array. Tensile tests were conducted in accordance with ASTM D638, followed by analysis of variance (ANOVA), signal-to-noise (S/N) ratio analysis, and scanning electron microscopy (SEM) fractography. The maximum experimental tensile strength of 24.38 MPa was achieved using the honeycomb infill shape, whereas the highest specific strength of 20.98 MPa·cm³/g was obtained at 70% infill density, demonstrating that the maximum tensile strength does not necessarily correspond to the highest material efficiency. ANOVA identified infill shape and infill density as the dominant factors affecting tensile strength, whereas infill shape and print speed primarily influenced specific strength. Optimum tensile strength of 26.538 MPa and a specific strength of 21.22 MPa·cm³/g were obtained during confirmation experiments, representing improvements of 8.85% and 1.14%, respectively, over the best original experimental values. SEM observations further revealed improved interlayer continuity and polymer fibrillization in the higher-strength specimens. These findings demonstrate the importance of considering both tensile performance and material efficiency when optimizing FFF parameters for PETG-CF components.
Power fitting failure is one of the major causes of power grid accidents. Conventional non-destructive testing methods, including ultrasonic testing and X-ray radiography, can only detect defects at the micrometer scale, leaving a very short warning window before catastrophic failure. Since all micrometer-scale defects originate from nanoscale defects, identifying nanoscale defects can significantly extend the early warning timeframe. Positron annihilation lifetime spectroscopy (PALS) is a sensitive technique for detecting nanoscale defects in metallic materials. In this study, Q235 steel, the most commonly used material for power fittings, was subjected to tensile deformation at different elongation levels (0, 1, 1.5, 2.0, and 3.5 mm). The evolution of nanoscale defects was investigated using PALS. The stress-strain curve showed that the Q235 sample fractured at an elongation of 3.5 mm. Two positron lifetime components were used to fitted the positron lifetime spectrum. The short lifetime (~160-220 ps) corresponding to positron annihilation in the bulk lattice, and a long lifetime (~0.5-1.5 ns) attributed to positron trapping at defects. The short lifetime remained nearly constant throughout deformation, while the long lifetime increased progressively with elongation, indicating the growth and coalescence of vacancy clusters. The mean positron lifetime exhibited two distinct plateaus: the first plateau (0-1.5 mm elongation) corresponded to gradual defect propagation, and the second plateau (2.0-3.5 mm elongation) reflected a rapid increase in defect size approaching fracture. These findings demonstrate that PALS can detect vacancy cluster evolution during tensile deformation and provide a quantitative criterion for assessing the proximity to fracture failure in power fitting materials.
