Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy

  • S. Ilangovan
  • R. Vaira Vignesh
  • R. Padmanaban
  • J. Gokulachandran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Castings of Copper–Nickel–Tin alloy were produced by varying the composition of Ni and Sn. The cast specimens were subjected to homogenization and solution treatment. The specimens were characterized for microstructure, hardness and subjected to adhesive wear test. Statistical regression model, artificial neural network model and Sugeno fuzzy model were developed to predict the hardness and wear rate of the alloy based on %Ni, %Sn and ageing time of the specimens. As Sugeno Fuzzy logic model uses adaptive neuro-fuzzy inference system, an integration of neural networks and fuzzy logic principles, the prediction efficiency was higher than statistical regression and artificial neural network model. The interaction effect of %Ni, %Sn and ageing time on the hardness and wear rate of the specimens were analysed using the Sugeno Fuzzy model.

Keywords

Spinodal alloy Hardness Wear Ageing time Regression Fuzzy logic Artificial neural network 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • S. Ilangovan
    • 1
  • R. Vaira Vignesh
    • 1
  • R. Padmanaban
    • 1
  • J. Gokulachandran
    • 1
  1. 1.Department of Mechanical Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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