This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization.

Abrasive Water Jet Machining (AWJM) is a popular machining method used to machine polymer matrix composites that are sensitive to temperature. This method is non-thermal, and each input parameter has a significant effect on output parameters, such as material removal rate, kerf width, surface roughness, and the potential for delamination. To ensure high-quality machining, it is crucial to set these input parameters at their optimal level. This paper proposes a simple approach to predict the optimum process parameters of water jet machining operations on jute fiber-reinforced polymer composite (JFRPC). The process parameters considered are standoff distance (SOD), traverse speed (TS), and abrasive material flow rate (MFR). Conversely, surface roughness (Ra) and delamination (Da) are the output parameters. Process parameters are set using Taguchi’s L27 array, with consideration given to three levels of each input parameter. The best value for process parameters is found using grey relational analysis (GRA), and an ANOVA on GRA illustrates the impact of each input variable. After a confirmation test, it was found that the suggested parameters guarantee the best possible results.

Carbon fibre-reinforced polymer (CFRP) composites have exceptional mechanical advantages such as high specific strength and stiffness, lightweight, and high damping capacity, making them very attractive for aircraft, aerospace, automotive, marine, and sporting applications. However, various defects such as delamination, burr formation, and surface roughness are observed during the drilling of CFRP composites, which are influenced by various drilling process parameters. In this work, the drilling quality of uni-directional CFRP composites. and the hybrid Al₂O₃ and hybrid SiC nano-composites are investigated experimentally using different types of drills such as step drill, core drill, and twist drill, as there is a limited study done on the comparative analysis of the impact of the above drills on the delamination factor and burr area on the above CFRP and hybrid nano-composites. The design of the experiment table was developed using response surface methodology (RSM) for input process parameters of spindle speed, feed, drill diameter, and drill type. The output surface characteristics (delamination factor and burr area) of the hole were measured quantitatively using the stereo zoom optical microscope. The main effect plots, contour plots, and analysis of variance (ANOVA) were used to examine the effect of spindle speed, feed, drill diameter, and drill type on exit delamination and burr formation. The analysis of main effect plots, contour plots, and analysis of variance showcased the optimum process parameters, such as a high spindle speed of 5500 rpm, low feed of 0.01 mm/rev, and drill diameter of 4 mm. The step drill demonstrated the least damage mechanism among drill geometries, followed by the twist and core drills. The minimum drilling damage was observed for the Al₂O₃ hybrid nano-composite compared to the neat CFRP composites.

Resin transfer molding (RTM) is a key process for manufacturing high-performance fiber-reinforced composites, in which resin infiltration dynamics play a critical role in process efficiency and defect minimization. This study presents a numerical and experimental analysis of resin flow in biaxial noncrimp carbon fiber reinforcement using FormuLITE 2500A/2401B epoxy. A model based on Darcy’s law and resin sorption effects was developed to investigate the influence of injection pressure (15–25 kPa), permeability (350 × 10⁻¹² m² to 0.035 × 10⁻¹² m²), porosity (0.78–0.58), viscosity (0.28–0.48 Pa·s), and injection radius (0.001–0.003 m) on flow-front progression. The results show that a higher injection pressure increased the infiltration depth by 30% at 250 s, while a 100× reduction in permeability reduced infiltration by 75%. The increased viscosity slowed the resin flow by ~18%, and the lower porosity reduced the flow-front progression by 15%. The experimental validation demonstrated a relative error of <5% between the numerical predictions and the measured data. This study provides critical insights into RTM process optimization for uniform fiber impregnation and defect minimization.

Aluminium and its alloys have exceptional corrosion resistant properties, simplicity of fabrication and extraordinary specific strength-to-weight ratio. The main objective of this study is to explore the effect of age hardening on the mechanical properties of tungsten inert gas (TIG) welded joints of the composite. This study highlights the enrichment in mechanical properties like hardness and tensile strength of TIG welded Al6061-SiC composites. A single-pass TIG welding using 5356 filler material is carried out on the composite with varying percentages of SiC having 25 µm. The hardness and tensile strength of the weld zone in as-weld condition is measured. The samples are then subjected to age hardening treatment at different temperatures and the peak hardness values are determined. The tensile strength is noted, and these values are compared with the values of the as-weld condition. The microstructure of the fracture surface in as-weld and peak aged condition is taken and the type of failure mode is found out. X-ray diffraction (XRD) spectra of the peak-aged sample is also considered for the analysis. The results indicated that there was significant influence leading to substantial improvement of hardness and tensile strength values of the samples after the age hardening process.

An aluminum 6061 (Al6061) metal matrix composite (MMC) reinforced with silicon carbide was prepared by stir casting. Specimens of the required dimensions were welded using the tungsten inert gas (TIG) method. ER5356 (Al-5%Mg) was chosen as the appropriate filler material for TIG welding. The input current parameter was varied (150, 170 and 200nA) while maintaining the other welding parameters at constant values. An assessment of the mechanical (tensile and impact strength) and microstructure properties of the TIG-welded Al6061 MMC with 6 wt. % silicon carbide particles was accomplished. An 8.27% improvement was observed in ultimate tensile strength (UTS) for the 150 A TIG-welded sample. UTS and elasticity decreased linearly with an increase in welding current but exhibited higher values than in non-welded specimens. The microstructural analysis of the welded MMCs showed a mixed mode of failure, with equiaxial dimples being dominant in lower-weld-current specimens. Compared to non-welded specimens, a 40% increase in impact strength was observed for the 150 A TIG-welded specimens, which decreased with an increase in the welding current value. SEM analysis revealed ductile striations and continuous river patterns, resulting in mixed failure.