In recent years, many researchers have focused on the preparation of carbon and basalt fiber-reinforced composites. As a result, the composites have gained popularity as an alternative to traditional materials such as wood, steel, and aluminum. Carbon and basalt fibers were used in a bidirectional woven mat, with particulates varying from 0 to 15 wt% nanoparticle SiC. The hybrid laminates were fabricated through vacuum bag infusion methods. The novelty of the research work lies in studying the influence of nanoparticle SiC-combined carbon and basalt fibers arranged in six stacking sequences, with LY556 used as polyester matrix. Specimens were prepared and tested as per ASTM standards. Tensile, flexural, impact, and hardness tests were performed on the obtained specimens and average values were obtained. It was found that 15% SiC filler addition enhanced (20%) the mechanical properties. Scanning electron microscope photos revealed the bonding between the fiber mat and the matrix of thecrystal structure. The obtained tensile strength was 346 MPa and the flexural strength was 388 MPa. Dynamic mechanical analysis showed that mechanical properties were improved with the addition of 15% SiCnanoparticles. Hence, this method can be used to manufacture structural applications and automotive parts.

In this work, wire electrical discharge machining (WEDM) of aluminum (LM25) reinforced with fly ash and boron carbide (B4C) hybrid composites was performed to investigate the influence of reinforcement wt% and machining parameters on the performance characteristics. The hybrid composite specimens were fabricated through the stir casting process by varying the wt% of reinforcements from 3 to 9. In the machinability studies, the WEDM process control parameters such as gap voltage, pulse-on time, pulse-off time, and wire feed were varied to analyze their effects on machining performance including volume removal rate and surface roughness. The WEDM experiments were planned and conducted through the L27 orthogonal array approach of the Taguchi methodology, and the corresponding volume removal rate and surface roughness were measured. In addition, the multi-parametric ANOVA was performed to examine the statistical significance of the process control parameters on the volume removal rate and surface roughness. Furthermore, the spatial distribution of the parameter values for both the responses were statistically analyzed to confirm the selection of the range of the process control parameters. Finally, the quadratic multiple linear regression models (MLRMs) were formulated based on the correlation between the process control parameters and output responses. The Grass–Hooper Optimization (GHO) algorithm was proposed in this work to identify the optimal process control parameters through the MLRMs, in light of simultaneously maximizing the volume removal rate and minimizing the surface roughness. The effectiveness of the proposed GHO algorithm was tested against the results of the particle swarm optimization and moth-flame optimization algorithms. From the results, it was identified that the GHO algorithm outperformed the others in terms of maximizing volume removal rate and minimizing the surface roughness values. Furthermore, the confirmation experiment was also carried out to validate the optimal combination of process control parameters obtained through the GHO algorithm.

Nickel-based superalloys are predominantly used to manufacture marine, automotive, and aerospace parts due to its excellent corrosion resistance in acidic environments and high-strength characteristics. Machining of these alloys are extremely difficult through conventional process because of their tendency to quickly work harden and poor thermal conductivity. Plasma arc cutting (PAC) is a disruptive metal-cutting process to perform cutting of difficult to cut materials even for intricate profiles. The present work examines the effect of PAC process variables such as arc current, gas pressure, cutting speed, and stand-off distance on evaluating the surface roughness, kerf width, and micro hardness of Monel 400™ superalloy. Experiments are designed and conducted using Box–Behnken design (BBD) of response surface methodology. The quadratic models are developed and assessed for its performance using analysis of variance (ANOVA). Optimal machining process conditions, cutting speed of 2427.08 mm/min, gas pressure of 3.83 bar, arc current of 45 A, and stand-off distance of 2.14 mm are obtained through multiobjective desirability approach. From the confirmation experiments, the relative error is found to be 2.68% for surface roughness, 4.45% for kerf width, and 4.36% for micro hardness which confirms the feasibility and efficiency of proposed methodology.

The present work aimed to investigate the dry sliding wear behaviors of hybrid polymer matrix composites made up of Kevlar, bamboo, palm, and Aloe vera as reinforcement materials of varying stacking sequences, along with epoxy as the matrix material. Three combinations of composite laminates with different stacking sequences such as AB, BC, and CA were fabricated by a vacuum-assisted compression molding process. The influence of composite laminates fabricated through various stacking sequences and dry sliding wear test variables such as load, sliding distance, and sliding velocity on the specific wear rate and co-efficient of friction were investigated. Experiments were designed and statistical validation was performed through response surface methodology-based D-optimal design and analysis of variance. The optimization was performed using grey relational analysis (GRA) to identify the optimal parameters to enhance the wear resistance of hybrid polymer composites under dry sliding conditions. The optimal parameters, such as composite combinations of CA, a load of 5 N, a sliding velocity of 3 m/s, and a sliding distance of 1500 m, were obtained. Furthermore, the morphologies of worn-out surfaces were investigated using SEM analysis.