Optimization on Cryogenic Co2 Machining Parameters of AISI D2 Steel using Taguchi Based Grey Relational Approach and TOPSIS

  • Authors

    • V Balaji
    • S Ravi
    • P Naveen Chandran
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16556
  • Cryogenic machining CO2, Design of experiments, Grey Relational Analysis, TOPSIS.ANOVA.

  • Abstract

    The Machinability, and the process parameter optimization of Cryogenic CO2 machining operation for AISI D2 steel have been investigated  based on the Taguchi based grey approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).In this examination work, the measure of the work materials utilized was AISI D2 Steel of size is 150mm × 50 mm × 50m with SANDWIK influence CVD To TiN coated carbide cutting insert tool device embed was utilized. The time taken for machining is 5 min and profundity of cut were kept up steady with various lower cutting velocities, and diverse encourage rate. An L27 orthogonal array was selected for planning the experiment. Cutting speed, depth of cut and feed rate were considered as input process parameters. Cutting force (Fz) and surface roughness (Ra) were considered as the performance measures. These performance measures were optimized for the improvement of machinability, quality of product. A comparison is made between the multi-criteria decision making tools. Grey Relational Analysis (GRA) and TOPSIS are used to confirm and prove the similarity. To determine the influence of process parameters, Analysis of Variance (ANOVA) is employed. The end results of experimental investigation proved that the machining performance can be enhanced effectively with the assistance of the proposed approaches.

     

     

     

  • References

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  • How to Cite

    Balaji, V., Ravi, S., & Naveen Chandran, P. (2018). Optimization on Cryogenic Co2 Machining Parameters of AISI D2 Steel using Taguchi Based Grey Relational Approach and TOPSIS. International Journal of Engineering & Technology, 7(3.12), 885-893. https://doi.org/10.14419/ijet.v7i3.12.16556

    Received date: 2018-07-30

    Accepted date: 2018-07-30

    Published date: 2018-07-20