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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Microstructure and Materials Properties
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
