Design of Cost-effective Compositionally Complex Alloys with Superior Mechanical Properties

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Professor Mingxing Zhangmingxing.zhang@uq.edu.au

The overall aim of this proposed project is to design and develop a new and cost-effective high entropy alloy (HEA) or compositionally complex alloy (CCA) with superior mechanical properties and therefore enable the industry applications of these types of metallic materials.

HEA is a relatively new member in the “family” of metallic materials.  The concept was firstly proposed in 2004. Although more 400 alloys have been designed and developed, their industrial applications are very limited due to the weakness of the materials, including high cost, low performance and property balance and lack of fundamental knowledge.  To overcome these limitations of HEAs/CCAs, this project firstly integrates fundamental knowledge of physical metallurgy of steels and the recent ground breaking research on grain refinement for cast metals into design of new and low-cost CCAs using the pseudo binary design strategy. Then, subsequent thermo-mechanical processes including heat treatment will be designed and optimized in order to achieve the superior properties.  In addition, fundamental research will also be conducted to investigate the solidification process of the new CCAs and to study the phase transformation and deformation mechanisms in the alloys during thermo-mechanical processing.

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of the science and engineering of metals would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of Materials Science and Engineering, Physics or Chemical Engineering or Mechanical Engineering and the potential for scholastic success.

A background or knowledge of machine learning and/or bigdata analytics is highly desirable.

*The successful candidate must commence by Research Quarter 2, 2021. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

Apply now