Rajesh Nayak

Hybrid polymer composites reinforced with basalt, hemp, and carbon nanotubes (CNTs) are highly valued in advanced engineering for their mechanical robustness, lightweight properties, and sustainability. This study utilizes a hybrid statistical framework to investigate the influence of machining parameters and reinforcement composition on burr formation, material removal rate (MRR), and surface roughness during the end milling of these composites. Machining analysis reveals that feed rate is the primary driver of both burr formation (42.50% contribution) and surface roughness (55.57%), as elevated rates induce severe plastic deformation. Spindle speed governs MRR (39.33%), improving surface finish through smoother chip removal, though excessive speeds increase thermal-induced burring. Higher axial depth of cut improves MRR but compromises overall surface integrity. Compositionally, ductile hemp fiber increases burr formation (29.57% contribution) and reduces MRR via higher energy absorption. Conversely, basalt fiber enhances stiffness, MRR, and surface finish. CNT incorporation strengthens interfacial bonding, mitigating delamination and improving surface integrity, albeit marginally reducing MRR due to elevated cutting resistance. Response surface methodology successfully predicted burr formation, MRR, and surface roughness with average errors below 9.74%, 10.14%, and 10.17%, respectively, while desirability functional analysis enabled multi-objective optimization. Crucially, analysis of variance highlighted significant synergistic interactions: feed rate–spindle speed interaction influenced burr formation ( p = 0.003), and CNT–hemp content significantly dictated both burr formation and surface roughness ( p < 0.001).

This paper investigates the AWJ machinability of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) fiber/epoxy-based hybrid composites by analyzing key machinability metrics such as kerf width (KW), material removal rate (MRR), and surface roughness (Ra). Various process parameters including CNT weight percentage, CNT diameter, stand-off distance, and traverse speed have been varied to optimize the machining performance. Experimental analysis suggested that increasing the CNT weight percentage significantly enhanced material hardness, thereby reducing both the MRR and surface roughness. Moreover, adjusting the stand-off distance and traverse speed further improved the machinability of the composite. ANOVA results highlighted that CNT weight percentage was a significant factor, accounting for 94.17% of the variation in MRR and 93.72% of the variation in surface finish, while the stand-off distance influenced 87.03% of the variation in kerf width. Additionally, response surface methodology (RSM) was utilized to develop predictive models that estimated KW, MRR, and Ra with error rates of 2.95%, 2.23%, and 5.65%, respectively. These insights offer a valuable framework for tailoring the AWJ process to achieve optimal machining outcomes in HRS/CNT/epoxy composite materials

This study examines the thermal properties and combustion behavior of HRS/CNT hybrid polymer composites, focusing on how variations in CNT weight percentage, CNT diameter, sonication time, and composite thickness affect thermal conductivity, degradation temperature, and burning rate. The experimental results demonstrate that a CNT weight percentage of 3% significantly enhances thermal conductivity, achieving a maximum value of 0.985 W m ⁻¹ ·K, while also elevating the thermal degradation temperature to a peak of 457 °C. These enhancements are due to the effective formation of a CNT network that facilitates efficient heat transfer and increases thermal stability. Larger CNT diameters, specifically 3 nm, contribute to improved thermal and flame-retardant properties, resulting in a reduced burning rate of 5.18 mm min ⁻¹ compared to 11 mm/min for composites with 1% CNT and 1 nm diameter. Optimal sonication time at 60 min ensures uniform CNT dispersion within the polymer matrix, further enhancing thermal conductivity and reducing burning rates. Additionally, thicker composites (20 mm) exhibit lower burning rates and higher thermal degradation temperatures, serving as more effective thermal barriers. BPANN prediction is succesfully applied with a neglegible error of 0.15% compared with 0.68% error of RSM predictions. This research provides valuable insights into the design and optimization of polymer composites with tailored thermal and combustion properties, offering potential applications in fields requiring high thermal stability and flame resistance.

The natural fiber-reinforced nanomaterial filler polymer matrix hybrid composite has superior applications in industrial and manufacturing fields due to its enhanced mechanical and machinability characteristics. However, in order to generate high-quality components, unconventional machining techniques, notably abrasive waterjet machining, have become more popular due to the inhomogeneity of composites, fiber pullout, greater surface roughness, and dimensional inaccuracy under traditional machining. Delamination typically refers to the separation that occurs along a plane parallel to the surface, such as the detachment of a coating from its underlying material or the separation between different layers within the coating itself. This paper investigates the AWJM characteristics of Hibiscus Rosa-Sinensis/Carbon nanotube/Epoxy (HRSCE)-based hybrid composite, focusing on delamination factors at entry, exit, and machining time. An L₂₇ orthogonal array was employed to optimize process parameters, revealing that DF-entry decreased with increasing CNT (wt.%), achieving its lowest values at 3 (wt.%) CNT and 2 mm stand-off distance due to enhanced composite toughness and precise jet focus. Conversely, DF-exit increased with higher CNT (wt.%), stand-off distance and traverse speed, attributed to the composite’s increased brittleness and reduced cutting efficiency. Machining time was predominantly influenced by CNT (wt.%) (92.4%), increasing with higher reinforcement levels due to enhanced material resistance. Response surface methodology models demonstrated high accuracy in predicting machining outcomes, with errors below 3%. Contour and surface plots identified optimal conditions for minimal delamination and machining time as 3 (wt.%) CNT, low stand-off distance (2 mm), and moderate traverse speed (200 mm/min). The SEM and optimal microscopy analysis confirmed that CNT reinforcement positively influenced fiber matrix interfacial integrity and reduced surface damage.

Understanding the drilling behaviour of composite materials is crucial for optimizing their manufacturing processes and enhancing their applicability across various industries. In this study, the drilling process of Hibiscus Rosa-Sinensis Polymer matrix composites is investigated due to the significance of investigating such advanced and sustainable composite materials for their potential applications. Hibiscus Rosa-Sinensis Fibers are extracted and processed from the outer bark of the hibiscus plant, are incorporated into polymer matrices in varying weight percentages (0 Wt%, 10 Wt%, 20 Wt%) to form discontinuously reinforced polymer composites. Samples with uniform dimensions of 150 × 75 × 15 mm, are used for the drilling operation using Ace Micromatic DTC-400 instrument. The project focuses on analysing the influence of key drilling input parameters such as Spindle speed, Feed rate and HRS (Hibiscus Rosa-Sinensis) Fiber weight percentage on the Thrust Force (N) and Torque (N-m) generated during drilling operations. Taguchi’s Design of Experiments with L27 orthogonal array is used to systematically optimize the input parameters to gain insights into the drilling behaviour of these composite materials. Further a second order mathematical model has been generated for Thrust Force and Torque using Response Surface Methodology (RSM). Thrust Force and Torque during drilling are measured using 9257 BA KISTLER Dynamometer coupled with DynoWare 2825 A software. The findings of this study not only contribute to a deeper understanding of the drilling process but also hold significant implications for industries reliant on composite materials. From aerospace to automotive sectors, where lightweight and durable materials are essential, to construction and renewable energy industries seeking sustainable alternatives, the application potential of hibiscus Rosa-Sinensis Fiber-reinforced composites is vast. By elucidating the intricate dynamics of drilling operations on these materials, this research paves the way for enhanced manufacturing processes and the development of advanced composite structures tailored to meet the demands of diverse industrial applications.

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L₂₇ orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use.

Incorporation of natural fibers with Fiber Reinforced Polymers (FRPs) is a promising avenue for sustainable and high-performance composite materials. The fibers, derived from outer bark portion of plants, offer significant merits such as renewability, low cost, and eco-friendly. Unique mechanical and physical properties, and widespread availability of Hibiscus Rosa-sinensis have made them subject of intense research interest. The present study investigated the chemically treatment of HRS Fibers using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO₄), and Acetic Acid (CH₃COOH) bat varying weight percentages (3, 4, 5 Wt%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. Fourier Transform Infrared (FT-IR) analysis revealed significant changes in O–H, C–H stretching, C=O moiety, aromatic ring, and C–O/C–C stretching. Potassium Permanganate treatment at 4 Wt% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. Further Single Fiber Pull-out test results in Potassium Permanganate at 4 Wt% and 3 h solutionizing time as best sample with highest Tensile Strength and Modulus. This research underscores the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications.

Die Sink Electric Discharge Machining is a widely used manufacturing process for shaping hard and electrically conductive materials. This study investigates the effects of various electrode materials such as, Ti-6Al-4V-SiCp, Brass and Copper on the machining performance of AISI 316 l Stainless Steel workpieces using EDM. The methodology involved optimizing parameters such as Electrode Material, Discharge Current, Gap Voltage, Spark Gap, Pulse-on Time, and Pulse-off Time. From the extensive experimantation it was observed that the combination of Ti-6Al-4V-SiCp electrode material, 8Amp Discharge Current, 90 V Gap Voltage, 75 μm Spark Gap, 100 μs Pulse-on Time and 15 μs Pulse-off Time has resulted in lowest electrode eear rate, higher machining time, and low electrode surface roughness ratio. Ti-6Al-4V-SiCp electrodes possess higher hardness and electrical conductivity compared to Brass and Copper Electrodes leading to higher wear resistance against repeated thermal shocks during electric discharge machining operation. Feed Forward Artificial Neural Network is successfully applied to predict the output characteristics of the experimentation with high accuracy of 98.3% (Electrode Wear Rate), 94.6% (Machining Time) and 93.8% (Electrode Surface Roughness Ratio). Further, microstructure analysis concludes that lowest wear is observed in Ti-6Al-4V-SiCp electrodes compared to Brass and Copper electrodes.