Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys

Reddy, N S and Panigrahi, Bharat Bhooshan and Ho, C M and Kim, J H and Lee, C S (2015) Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys. Computational Materials Science, 107. pp. 175-183. ISSN 0927-0256 (In Press)

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Abstract

An artificial neural network model was developed to correlate the relationship between the alloying elements (Al, V, Fe, O, and N) and heat treatment temperature (inputs) with the volume fractions of α and β phases (outputs) in some α, near-α, and α + β titanium alloys. The individual and combined influences of the composition and temperature on α and β phases were simulated through performing sensitivity analysis. A new method has been proposed to estimate the relative importance of the inputs on the outputs for single phase α-Ti, near-α Ti, and α + β Ti alloys. The average error of the model predictions for 35 unseen test data sets is 1.546%. The estimated behavior of volume fractions of α and β phases as a function of composition and temperature are in good agreement with the experimental knowledge. Justification of the results from the metallurgical interpretation has been included.

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IITH Creators:

IITH Creators	ORCiD
Panigrahi, Bharat Bhooshan	UNSPECIFIED

Item Type:

Article

Additional Information:

N.S. Reddy acknowledges Prof. G.S. Chauhan, Department of Chemistry, Himachal Pradesh University, India for his valuable suggestions and editing manuscript and Y. Kiran Kumar, Wipro limited, London, United Kingdom and J. Krishnaiah, BHEL, Tiruchirappalli, India for their help in ANN model development. N.S. Reddy greatly thankful to the learned anonymous reviewers for their enormous constructive criticism on this manuscript. Their suggestions improved the quality of the manuscript and also enhanced the author’s knowledge of neural networks modeling.

Uncontrolled Keywords:

Titanium alloys; Microstructure; Neural networks; Index of relative importance

Subjects:

Others > Metallurgy
Materials Engineering > Materials engineering

Divisions:

Department of Material Science Engineering

Depositing User:

Team Library

Date Deposited:

07 Jul 2015 06:03

Last Modified:

10 Nov 2017 06:30