Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

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.

In recent years, abrasive waterjet machining has emerged as a promising machining technique for the machining of composites because of its non-thermal nature. In the present study, the effect of machining parameters on the quality of machining has been studied and the process parameters have been optimized to machine jute-fiber-reinforced polymer composites. The design of the experiment was used to predict the combination of the input parameters for L27 experiments. Taguchi and response surface methodology (RSM) techniques were employed to analyze the experimental data and identify the optimum combination of process parameters to achieve as little delamination as possible. The results indicate that an increase in the values of the parameters traverse speed and abrasive mass flow rate leads to an increase in the damage obtained. However, an increase in the value of the parameter stand-off distance minimizes the damage produced. To achieve minimum delamination, the optimum combination of input parameters is obtained through Taguchi and RSM. For the present experimental condition, to achieve minimum delamination, the parameter traverse speed should be set at 20 mm/min, the stand-off distance should be 4 mm, and the abrasive flow rate should be set at 0.25 kg/min. The results confirm that the optimum combination of parameters obtained through both approaches is similar. This investigation results indicated a significant improvement in the cutting quality with reduced damage, achieved through the optimized process parameters. For the considered range of parameters, graphs are plotted such that any intermediate values can be anticipated within the considered range without performing any further experiments. The present work signifies the effect of fiber orientation on delamination.

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.

This study presents a comprehensive investigation into the fabrication and characterization of high‐density polyethylene (HDPE) composites reinforced with zirconia (ZrO₂)‐coated multi‐walled carbon nanotubes (MWCNTs). HDPE, a widely used polymer for pressure pipe applications, was selected for its superior mechanical properties, while ZrO₂‐coated MWCNTs were employed as nanofillers to enhance these properties further. The acid functionalization of MWCNTs, followed by the application of a ZrO₂ coating, was executed to ensure optimal dispersion and interfacial bonding within the HDPE matrix. The functionalized MWCNTs (f‐MWCNTs) were synthesized via an acid treatment process using 8 M HNO₃, which significantly eased agglomeration and facilitated a stable ZrO₂ coating. Subsequent hydrothermal processing at 200°C for 18 h yielded uniformly coated MWCNTs. These nanofillers were then melt‐blended with HDPE, followed by injection molding. Mechanical testing revealed substantial enhancements in the tensile, flexural, and hardness properties of the composites. The tensile strength exhibited a marked increase, peaking at a 3 wt.% ZrO₂‐coated MWCNT concentration, with a 50% improvement over pristine HDPE. Flexural modulus and hardness values also saw significant increments, with the highest enhancements recorded at 4 wt.% filler content. Impact testing showed improved toughness, although excessive filler loading led to diminished returns due to nanoparticle aggregation. FTIR and XRD analyses supported the successful integration of ZrO₂‐coated MWCNTs within the HDPE matrix, highlighting the presence of characteristic peaks associated with both MWCNTs and HDPE. These findings provide the potential of ZrO₂‐coated MWCNTs as effective nanofillers in enhancing the mechanical performance of HDPE composites, making suitable for advanced engineering applications.Highlights: Coating of ZrO₂ on MWCNTs using Hydrothermal method. Melt blending of ZrO₂ coated MWCNT with HDPE using twin‐screw extruder. Preparation of samples through injection molding as per ASTM standard. Mechanical properties and characterization analysis of the composite.

Basalt fibers (BFs) are being explored due to their excellent thermo-mechanical properties and commercial value. However, additional surface modifications are needed to improve the interfacial adhesion of the fibers with the matrix. Therefore, the present work explored the applicability of nitric acid, oxalic acid, and silane reagents as surface modification agents. The fibers were subjected to immersion in the chemicals for a varied length of time. The laminates were fabricated using a wet layup process and compression-cured. The developed composites were subjected to mechanical testing to ascertain the effect of the selected surface treatment acids and the treatment duration on the tensile, impact, and flexural strength. Acid treatment was found to be beneficial for improving the mechanical properties of the laminates due to the generation of several functional groups on the surface of the acid-treated fibers. The mechanical properties of the laminate improved for nitric acid treatment in comparison to oxalic acid treatment. With respect to time, the mechanical properties of the laminate improved when nitric acid treatment time was increased to 30 min from 15 min. However, when oxalic acid treatment was performed for 180 min instead of 90 min, there was an observed reduction in the mechanical properties of the laminates. The highest increase in the tensile strength was noted in composites manufactured using fibers subjected to primary acid treatment and secondary silane treatment. Conclusively, nitric acid treatment was most beneficial as it helped improve the tensile, impact, and flexural strength of the BF/epoxy composites.