This project will deploy artificial intelligence and deep learning to evaluate structural integrity and predict fatigue life of additively manufactured aerospace metal alloys.
To address this limitation, this project aims to develop an innovative fatigue damage model that incorporates detailed defect characteristics using machine learning and multiscale modeling. High-resolution X-ray computed tomography (CT) will be employed to observe and quantify the dynamic changes of defects in AM Ni-based alloys with varying porosities and printing orientations during fatigue testing.
Closes 31st December 2026.
One scholarship available.
Master by Research degree; or a Master by Coursework degree with a significant research component graded as high distinction or equivalent; or a Bachelor Honours degree achieving first class honours; in Engineering (Aerospace, Mechanical, Materials, Manufacturing), or Science (Physics, Chemistry), or another suitable field;
To apply, please submit the following documents to Professor Raj Das (email@example.com):
A CV including any education, marks/grades, relevant professional experience, publications (if any), awards (if any), and names of two referees.
Knowledge and Skills: Knowledge and background in either solid mechanics, material engineering or finite element analysis is desireable.
Acknowledgement of Country
RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.