• Title of article

    Fabrication process optimization for improved mechanical properties of Al 7075/SiCp metal matrix composites

  • Author/Authors

    Das، Dipti Kanta نويسنده School of Mechanical Engineering, KIIT University, Bhubaneswar-24, Odisha, India , , Mishra ، Purna Chandra نويسنده School of Mechanical Engineering, KIIT University, Bhubaneswar-24, Odisha, India , , Chaubey، Anil Kumar نويسنده CSIR-Institute of Minerals & Materials Technology, Bhubaneswar-751013, Odisha, India , , Singh، Sranjit نويسنده School of Mechanical Engineering, KIIT University, Bhubaneswar-751024, Odisha, India ,

  • Issue Information
    ماهنامه با شماره پیاپی 52 سال 2016
  • Pages
    12
  • From page
    297
  • To page
    308
  • Abstract
    Two sets of nine different silicon carbide particulate (SiCp) reinforced Al 7075 Metal Matrix Composites (MMCs) were fabricated using liquid metallurgy stir casting process. Mean particle size and weight percentage of the reinforcement were varied according to Taguchi L9 Design of Experiments (DOE). One set of the cast composites were then heat treated to T6 condition. Optical micrographs of the MMCs reveal consistent dispersion of reinforcements in the matrix phase. Mechanical properties were determined for both as-cast and heat treated MMCs for comparison of the experimental results. Linear regression models were developed for mechanical properties of the heat treated MMCs using list square method of regression analysis. The fabrication process parameters were then optimized using Taguchi based grey relational analysis for the multiple mechanical properties of the heat treated MMCs. The largest value of mean grey relational grade was obtained for the composite with mean particle size 6.18 µm and 25 weight % of reinforcement. The optimal combination of process parameters were then verified through confirmation experiments, which resulted 42% of improvement in the grey relational grade. Finally, the percentage of contribution of each process parameter on the multiple performance characteristics was calculated through Analysis of Variance (ANOVA).
  • Journal title
    Management Science Letters
  • Serial Year
    2016
  • Journal title
    Management Science Letters
  • Record number

    2382615