Multi-Objective Optimization and Modeling of Surface Roughness in Inconel 718 using Taguchi Grey Relational Analysis and Response Surface Methodology

  • Authors

    • Abhijith S
    • Srinivasa Pai P
    • Bhaskara Achar
    • Grynal D’mello
    https://doi.org/10.14419/ijet.v7i3.34.19461
  • Surface roughness, Cutting speed, feed, RSM, GRA

  • Abstract

    Nickel-based super-alloys have been widely used in aircraft, nuclear industry, transfer rolls, single crystal turbine blades, heat treating trays, and die blocks due to their thermal resistance and their ability to retain mechanical characteristics at high temperatures. In this work, dry turning experiments on Inconel 718 have been performed using uncoated carbide inserts at various cutting speeds, feeds and a constant depth of cut. Taguchi based Grey Relational Analysis (GRA) optimisation has been used to optimise the surface roughness parameters namely Ra and Rt. Taguchi GRA has established optimal machining conditions for machining Inconel 718 considering cutting tool vibrations, temperature and tool wear as input parameters. The optimised machining conditions are 80m/min cutting speed and 0.1mm/rev feed rate, and considering other parameters, it is 9 g for cutting  vibration, 95ºC for temperature and 0.08mm for tool wear. Analysis of Variance (ANOVA) showed that feed rate (70.35%) is the most significant factor influencing surface roughness parameters followed by cutting speed (16.12%), tool wear (9.8%), vibrations (3.4%) and temperature (0.4%). Response Surface Methodology has been used to develop multiple regression models to predict surface roughness. The quadratic model developed has a R2 value of 0.917 and results in a prediction accuracy of 75% for Ra and R2 value of 0.906 with prediction accuracy of 75% for Rt.

     

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  • How to Cite

    S, A., Pai P, S., Achar, B., & D’mello, G. (2018). Multi-Objective Optimization and Modeling of Surface Roughness in Inconel 718 using Taguchi Grey Relational Analysis and Response Surface Methodology. International Journal of Engineering & Technology, 7(3.34), 724-728. https://doi.org/10.14419/ijet.v7i3.34.19461

    Received date: 2018-09-11

    Accepted date: 2018-09-11