Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

Multiscale microstructure modelling for nickel based superalloys

Basoalto, Hector and Brooks, Jeffery and Di Martino, I (2009) Multiscale microstructure modelling for nickel based superalloys. Materials Science and Technology, 25 (2). pp. 221-227. ISSN 0267-0836

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

The present paper is concerned with the development of multiscale modelling approaches for predicting the microstructural evolution and high temperature deformation characteristics of superalloys with special attention to creep and hot forming behaviour. A microstructure informed deformation model is presented that links rearrangements at the microscale to the overall macroscopic response of the material through a damage mechanics approach and results are presented on the application of the model to CMSX4. The control of microstructure, during the manufacture of nickel based superalloy components, is key to the development of the mechanical properties required for the high temperature applications typical of these materials. Results from empirical methods and a new physics based approach for modelling recrystallisation in polycrystalline superalloys are presented for the prediction of the grain size distributions produced during hot forming operations in Inconel alloy 718. A global macroscale modelling approach based on Neural Networks has been developed which includes the effects of composition, heat treatment and processing route and the effectiveness of the model for both property prediction and interpolation is demonstrated