Search Results (2)

In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L₂₇ orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithm’s effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithm’s optimal combination of turning parameters. Full article

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. Full article