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, B B 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 CreatorsORCiD
Panigrahi, B BUNSPECIFIED
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: 07 Jul 2015 06:03
URI: http://raiith.iith.ac.in/id/eprint/1658
Publisher URL: http://dx.doi.org/10.1016/j.commatsci.2015.05.026
OA policy: http://www.sherpa.ac.uk/romeo/issn/0927-0256/
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