Abstract
A model is described in detail for the prediction of properties of titanium alloys at different temperatures as functions of processing parameters and heat treatment cycles. The chapter also discusses how to use the model for the optimisation of processing and heat treatment parameters. It continues with introducing a separate model, developed for prediction of fatigue stress life S-N diagrams for Ti-6Al-4V alloy under various conditions, again using an artificial neural network. The third part of the chapter shows a model for the prediction of the correlation between alloy composition and microstructure and tensile properties in γ-based titanium aluminide alloys using ANN.
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: Titanium alloys: modelling of microstructure, properties and applications
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.
