Abstract
Objective: In this work, the design parameters that significantly affect the machinability of EN31 alloy steel using coated carbides were analysed and optimized using multi response methodology. Methods/Statistical Analysis: The EN31 steel is machined in the dry cutting environment using the coated carbide insert and their evaluation is studied. The experiments were conducted with three significant parameters namely cutting speed, feed rate and depth of cut. These parameters are optimized using Taguchi based grey relational analysis and their effects of parameters on surface roughness, interface temperature and flank wear were examined. Findings: The result reveals that all three input variables have influence over surface roughness, interface temperature and flank wear. ANOVA indicates that the output response is greatly influenced by cutting speed followed by feed and depth of cut. The results at optimum condition were predicted and it is found to be closer to the experimental results. From the analysis, it has been found that the coated carbide exceeds the performance compared to the uncoated carbides. Application/Improvements: The optimum parameters obtained from this experiment are effective in improving the machinability of EN31 alloy steel in the industry that minimizes cost and time. Keywords: Alloy Steel, ANOVA, Grey Relational Analysis, Hard Turning, Machinability
Highlights
In recent days, various manufacturing industries works in the machining of hardened steel toward the improvement of product quality
In5 investigated the machinability of hardened steel during both hard and soft turning using ceramic tools mixed with TiC and alumina
In11 investigated the effect of power consumption and surface roughness generated in hard turning of EN31 alloy steel under different parameters with tungsten carbide tool coated with TiN/Al2O3/TiCN
Summary
Various manufacturing industries works in the machining of hardened steel toward the improvement of product quality. In5 investigated the machinability of hardened steel during both hard and soft turning using ceramic tools mixed with TiC and alumina He observed the cutting force and the interfacial temperature to be high and surface roughness to be low for harder work piece. The surface quality is better for the insert coated with multilayer TiN when compared to the cylindrical grinding He suggested that the tool wear and surface roughness is being affected with the cutting speed and the feed. In11 investigated the effect of power consumption and surface roughness generated in hard turning of EN31 alloy steel under different parameters with tungsten carbide tool coated with TiN/Al2O3/TiCN They found that the power consumption and the surface roughness is being affected with all the input parameters such as cutting speed, feed and depth of cut. The various output responses were optimized using multi-response methodology like Grey Relational Analysis
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