Title: Microstructure evaluation and modelling the tensile strength and yield strength of titanium alloys

Authors: P.S. Noori Banu; S. Devaki Rani

Addresses: Department of Metallurgical Engineering, Jawaharlal Nehru Technological University-Hyderabad, Hyderabad, 500085, India ' Department of Metallurgical Engineering, Jawaharlal Nehru Technological University-Hyderabad, Hyderabad, 500085, India

Abstract: The rationale of the current study was to develop artificial neural network (ANN) models of titanium alloys for predicting tensile strength and yield strength using the alloy composition and processing parameters as the inputs and validate the models through experimental evaluation and correlate with microstructure characteristics. The robustness of the model was tested through experimental evaluation of tensile strength in Ti-6Al-4V, Ti-5.8Al-4Sn-3.5 Zr-0.7Nb-0.5Mo-0.3 Si, Ti-3Al-8V-4Zr-6Cr-4Mo, and Ti-10V-2Fe-3Al alloys. Microstructure characteristics i.e., volume fraction of alpha, alpha grain area and Feret ratio of Ti, Ti-6Al-4V, Ti-6Al-5V, Ti-6Al-6V-2Sn, Ti-8Mn, and Ti-13V-11Cr-3Al correlated inversely with predicted tensile strength and yield strength. This three-tier validation ensures efficient performance of the developed ANN models.

Keywords: ANN; artificial neural network; titanium alloys; tensile/yield strength; microstructure; volume fraction alpha; alpha grain area; Feret ratio.

DOI: 10.1504/IJMMP.2019.098112

International Journal of Microstructure and Materials Properties, 2019 Vol.14 No.1, pp.14 - 27

Received: 12 Feb 2018
Accepted: 04 Sep 2018
Published online: 04 Mar 2019 *

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