Surface modified montmorillonite nanoclay created tortuous path to flow of moisture and enhanced bonding of PALF with epoxy, which resulted in lower diffusion coefficient and reduced impact of moisture diffusion in epoxy PALF nanoclay composites.

Carbon-fiber-reinforced epoxy composite (CEC) has gained widespread acceptance as a structural material in various applications. Drilled holes are essential for assembling composite material components. Reducing drilling-induced damage and temperature effects is crucial for improved surface quality and integrity of the drilled composite. In the present work, drilling experiments were conducted on CEC, hexagonal-boron nitride (h-BN) dispersed CEC, and molybdenum disulfide (MoS₂) dispersed CEC at three different levels of spindle speed, feed, and drill diameter using solid carbide twist drills. The filler concentrations used in this study were 4, 6, and 8 wt%. Analysis of variance (ANOVA) was used to determine the significance of input factors (feed, spindle speed, drill diameter, and filler concentration) on the drilling responses such as thrust force, temperature, arithmetic mean surface roughness (Ra), and push-out delamination factor (DFexit). The average drilling temperature, Ra, and DFexit of MoS₂ dispersed CEC were reduced by 24.7, 46.5, and 11.3%, respectively, when compared to neat CEC. In h-BN dispersed CEC, the average drilling temperature, Ra, and DFexit were reduced by 25.2, 40.9, and 13.2%, respectively, compared to neat CEC. The lubricating properties and high thermal conductivity of filler added to epoxy are responsible for the lower temperature and improved hole surface finish. The improved delamination resistance in filler-loaded CEC is due to the strengthening of the matrix and fiber–matrix interface. Scanning electron microscopy (SEM) was used to examine the morphology of the drilled composite surface. The spindle speed of 5500 rpm, feed of 0.03 mm.rev⁻¹, and filler loading of 4 wt% produced the minimum Ra and DFexit. The response surface method (RSM) was applied to determine the input parameters based on multi-response optimum criteria.

Carbon fiber-reinforced polymer (CFRP) composites have gradually replaced metals due to their exceptional strength-to-weight ratio compared to metallic materials. However, the drilling process often reveals various defects, such as surface roughness, influenced by different drilling parameters. This study explores the drilling quality of uni-directional CFRP composites, as well as hybrid Al₂O₃ alumina and hybrid SiC silicon carbide nano-composites, through experimental exploration using step, core, and twist drills. Response surface methodology (RSM) and statistical tools, including main effect plots, ANOVA, contour plots, and optimization techniques, were used to analyze the surface roughness of the hole. Optimization plots were drawn for optimal conditions, suggesting a spindle speed of 1500 rpm, feed of 0.01 mm/rev, and a 4 mm drill diameter for achieving minimum surface roughness. Furthermore, two machine learning models, artificial neural network (ANN) and random forest (RF), were used for predictive analysis. The findings revealed the robust predictive capabilities of both models, with RF demonstrating superior performance over ANN and RSM. Through visual comparisons and error analyses, more insights were gained into model accuracy and potential avenues for improvement.

Plastics reinforced with glass fiber have a significant likelihood of being replaced by natural fiber hybrid composites (NFHCs). Making holes helps in part assembly, which is a crucial activity in the machining of composite constructions. As a result, choosing the right drill bit and cutting parameters is crucial to creating a precise and high-quality hole in composite materials. The present study employs the Taguchi approach to examine the delamination behavior and hole quality of ramie–bamboo composite laminates consisting of epoxy and nano-fillers (SiC, Al₂O₃) with feed, spindle speed, and three distinct drill bit types. Surface roughness and delamination are significantly influenced by feed and spindle speed, as indicated by the results of the analysis of variance. It was found that the spindle speed had a major impact on the delamination factor and surface roughness, while the feed and drill bit type had a minor influence. The surface roughness (76.5%) and delamination factor (66.7%) are significantly affected by the spindle speed.

AlSi10Mg’s superior strength-to-weight ratio has attracted significant interest from the defence, automotive, and aerospace industries. Optimizing material performance and enhancing component reliability require accurate prediction of wear behaviour under various operating conditions. This work models and predicts the wear loss of additively manufactured AlSi10Mg produced by Laser Powder Bed Fusion (LPBF) using a data-driven approach. A total of 16 experimental samples were evaluated based on an L16 experimental design, considering applied load, sliding distance, and sliding speed as the primary test variables. The measured tribological responses included wear rate, coefficient of friction (COF), and friction force. To assess the material’s tribological performance, pin-on-disc wear experiments were conducted at various loads, sliding speeds, and sliding distances. Microstructural characterization was performed before and after wear testing, including analyses of porosity and grain size, to investigate the relationship between material structures and wear behaviour. Machine learning models based on Linear Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, Gaussian Process, Decision Tree, Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) were then developed and compared using the experimentally recorded wear loss data. An 80:20 train–test split was adopted for machine-learning analysis, with the models developed specifically to predict wear loss/wear rate from the experimental input variables. The combined experimental, microstructural, and data-driven approach provides useful insights into the wear behaviour of LPBF-fabricated AlSi10Mg. Gradient Boosting and Gaussian Process demonstrated promising predictive performance, while the results from the 16-experiment L16 dataset highlight the potential of machine learning for preliminary wear prediction. Further experimental data can support the development of more robust and generalizable predictive models.

Electric vehicle battery enclosure systems under vibrational loading may experience structural damage, reducing the lifespan of lithium-ion cells. Honeycomb sandwich structures are widely used in EV battery systems due to their high stiffness-to-weight ratio and superior vibration performance. The present study is undertaken to investigate the dynamic behaviour of honeycomb sandwich panels using ANSYS Workbench. A three-dimensional model of a sandwich structure consisting of an aluminium core and composite face sheets is developed in ANSYS. Modal analysis and power spectral density-based random vibration analysis are performed to examine the impact of face-sheet thickness (0.5–2.5 mm) and layup configuration on structural performance. The modal analysis reveals that the natural frequencies increase considerably with thickness up to 1.5 mm due to increased bending stiffness. The results of random vibration analysis demonstrate a substantial reduction in total deformation and equivalent stress with increasing thickness. Among the configurations studied, the [0C/0G]/Core/[0G/0C] layup demonstrates the most favourable response under the present PSD loading condition due to fibre alignment with the principal loading direction. The findings are further converted into practical design guidelines for electric vehicle battery enclosures, including an appropriate face-sheet thickness range of 1.0–1.5 mm and fibre orientations aligned with the principal loading direction, while considering the associated mass penalty. These findings provide a design-oriented framework for selecting thickness and layup configuration to achieve a practical balance between vibration resistance and weight.