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Predicting failure in crystalline materials using machine learning techniques

About This PhD Project

Project Description

Single crystal materials are extensively used in components ranging from the smallest (e.g. micro-lens, MEMS devices) to the largest sizes (e.g. turbine blades). What is imperative in such components is to ensure safety and reliability of performance during the lifetime of the product. This implies that engineers would need a robust, accurate and fast numerical model which is able to assess strain accumulation in parts when subjected to complex loading states. These ‘hotspots’ are precursor to failure. Knowledge of its location and state will allow for precautionary measures to be incorporated during maintenance and inspection.

The project will deal with integrating crystal plasticity models and machine learning techniques to gain insight into the microstructural effects of single-crystal metals. Robust computational models already exist. These will be enhanced to ‘hand-shake’ with a machine learning framework. Necessary small-scale experiments will be conducted in a nano-micro indentation testing machine available.
Once developed the computational framework will be used to predict failure in complex geometries under complex loading states which are of industrial relevance.

Entry requirements

- At least a 2:1 Honours degree (or equivalent e.g. GPA of 7.5/10 or higher) in Mechanical Engineering, Materials Engineering, Aerospace Engineering, Civil Engineering or a related subject
- A relevant master's degree and/or experience in one or more of the following would be an advantage: mechanical engineering, product design, materials engineering, aerospace engineering, civil engineering or a related subject.

How to apply

All applications should be made online at Under programme name select Mechanical and Manufacturing Engineering. Please quote reference number: ARUF2019

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