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.

Modern research focuses on natural, green, and sustainable materials that can be used to replace conventional materials. Because of their beneficial qualities, natural fibre composites are being thoroughly researched. This research focuses on the development of a flax fibre reinforced with phenol-formaldehyde resin hybridization with ramie fibre through a vacuum infusion process. Eight different sequences were fabricated using a core–sheath structure and were mechanically characterized as per ASTM standards. The fabrication technique influences the adhesion of the matrix with reinforcement. The results also reveal that composite having ramie as a sheath layer and flax as a core delivers good mechanical characteristics compared to vice versa. The laminate H exhibited highest mechanical properties among all the eight laminates produced for this study. It exhibited a tensile strength of 54 MPa, tensile modulus of 0.98 Gpa, elongation of 7.1%, flexural strength of 143 Mpa, and compressive strength of 63.65 Mpa. The stress strain curves revealed that all the laminates exhibited ductile behaviour before failing during the tensile test and flexural test, respectively. The stacking sequence of the laminate H influenced the mechanical properties exhibited by it and its counterparts. A morphological study was carried out to analyse the failure surfaces. Morphological analysis exhibited few defects in the laminate after the tests. The composites developed delivers better mechanical properties than commercial composites available on the market, which can be used in lightweight structural applications.

Plant-derived fibres, called lignocellulosic fibres, are a natural alternative to synthetic fibres in polymer composite reinforcement. Utilizing renewable resources, such as fibre-reinforced polymeric composites made from plant and animal sources, has become a crucial design requirement for developing and producing parts for all industrial goods. Natural-fibre-based composites are used for door panels, trays, glove boxes, etc. This study involves developing and thermal analysing a flax fibre reinforced with phenol–formaldehyde resin hybridization with ramie fibre by way of a vacuum infusion process. As per ASTM Standard, eight different sequences were fabricated and thermally characterized. In the present study, three stages of weight loss (%) are shown by the thermogravimetric analysis (TGA). The sample loses less weight during the first stage, more during the second, and more during the third. The sample’s overall maximum temperature was recorded at 630 °C. It was discovered that sample D (80.1 °C) had the highest heat deflection temperature, and sample B had the lowest (86.0 °C). Sample C had a low thermal expansion coefficient, while sample G had a high thermal expansion coefficient. Sample E had the highest thermal conductivity, measured at 0.213 W/mK, whereas sample A had the lowest conductivity, at 0.182 W/mK. From the present study, it was found that sample H had better thermal characteristics. The result of the present investigation would generate thermal data regarding hybrid ramie and flax composites, which would be helpful for researchers and practitioners involved in the field of biocomposites.

The different sizes of waste tyre rubber particles and precipitated silica-filled natural rubber composites were investigated to evaluate the vibration damping behavior of the waste tyre rubber filled rubber composites. In this work, waste tyre rubber particles prepared in three different sizes (100–250, 550–700, and 1000–1150 µm) using ambient grinding process. Then the particles blended with natural rubber and silica compounds, and the blend was synthesized by two roll mill and hydraulic press vulcanization. The various proportions of prepared samples in all sizes were characterized in respect of their vibration damping behavior. The results indicated that large size of waste tyre rubber-filled natural rubber composites showed excellent vibration damping performance compared with other sizes used and were evaluated by both direct and indirect methods. Differential scanning calorimeter study showed that, use of large waste tyre rubber provided more energy absorption than other sizes used which led to increase the damping characterization. Owing to excellent damping performance, large waste tyre rubber-filled natural rubber composites accurately used in the applications where vibration damping is considered as important design criterion.

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.