Optimum Selection of Process Parameters in EN-31Alloy Steel for Surface Roughness and MRR using Taguchi Method

  • Authors

    • Rajesh Kumar Maurya
    • M. S.Niranjan
    • Nagendra Kumar Maurya
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.24122
  • Analysis of Variance, EN-31alloy steel, Grey relational analysis, Signal to noise (S/N) ratio, surface roughness, Taguchi Method,

  • Abstract

    Surface roughness and material removal rate (MRR) plays a vital role for precision manufacturing as well as for better productivity. The process parameters such as cutting speed ,feed rate and depth of cut has been optimized on CNC Lathe for the response surface    roughness and material removal rate using Taguchi method .The experiments have been conducted on EN-31 alloy steel using  L9 (33) orthogonal array. The performance characteristics of process   parameters have been seen by using analysis of variance (ANOVA) for minimum surface roughness and maximum material removal rate (MRR).The result divulge that the cutting speed has more influential effect on surface roughness of EN-31 steel alloy followed by feed rate and has minimum effect of depth of cut on the same. Whereas it has been seen that, in case of material removal rate depth of cut and feed rate have the more influential effect as compare to the cutting speed. The novelty of this research work lies in the fact that no such study have been carried out by using these process parameters in the archival literature. The Grey relational analysis (multi-objective optimization) technique was employed to optimize the response factors.   The optimum process parameters condition was found as cutting speed 2000 rpm, feed rate 200 mm/rev and depth of cut 0.8mm.

     

     

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  • How to Cite

    Kumar Maurya, R., S.Niranjan, M., & Kumar Maurya, N. (2018). Optimum Selection of Process Parameters in EN-31Alloy Steel for Surface Roughness and MRR using Taguchi Method. International Journal of Engineering & Technology, 7(4.39), 447-453. https://doi.org/10.14419/ijet.v7i4.39.24122

    Received date: 2018-12-16

    Accepted date: 2018-12-16

    Published date: 2018-12-13