PhD Studentship: ML-assisted detection of micromechanical fracture in bioinspired composites

at University of Surrey
Published September 19, 2023
Location Guildford, United Kingdom
Category Machine Learning  
Job Type Scholarship  

Description

PhD Studentship:

Machine learning (ML)-assisted detection of micromechanical fracture in bioinspired composites

This collaborative research project, conducted in partnership with the National Physical Laboratory (NPL), focuses on revolutionising micromechanical fracture detection within bioinspired composites through the innovative use of machine learning techniques. These bioinspired composites exhibit remarkable mechanical properties with diverse industrial applications. However, ensuring their structural reliability necessitates precise detection and assessment of cracks during micromechanical fracture toughness testing.

The intricate microstructure and heterogeneous characteristics of these composites pose unique challenges to automated crack detection. These challenges are further compounded by the resource-intensive processes of data acquisition and labelling. This pioneering initiative aims to harness the power of transfer learning by adapting pre-trained deep learning models, initially designed for general image analysis, to the intricacies of crack detection within bioinspired composites.

Our objectives encompass both technological advancement and practical applicability.

We aim to:

  1. Investigate the effectiveness of transfer learning in elevating the accuracy of crack detection during micromechanical fracture toughness testing in bioinspired composites.
  2. Develop a robust transfer learning framework that integrates pre-trained deep learning architectures and tailors them for precise crack detection within these challenging materials.
  3. Identify optimal strategies for fine-tuning pre-trained models, addressing the unique challenges of crack detection within bioinspired composites.
  4. Conduct comprehensive evaluations to quantitatively measure the framework's performance, employing metrics such as Intersection over Union (IoU) and F1 score. A comparative analysis against traditional crack detection methods will highlight the superiority of transfer learning.

The potential impact of this research is substantial. Successful implementation could significantly advance the field of materials science and engineering by enabling more accurate and efficient assessments of fracture toughness and structural reliability in bioinspired composites. Moreover, the transfer learning framework developed here could pave the way for analogous advancements in other materials with complex microstructures, amplifying its influence across diverse industries.

Supervisors

Dr Yinglong HeDr Tan SuiAbdalrhaman Koko

Entry requirements

Open to any UK or international self-funding candidates starting in January 2024.

Later start dates are possible.

You will need to meet the minimum entry requirements for our PhD programme.

Visit our website for a full candidate profile and list of responsibilities.

How to apply

Applications should be submitted via the Engineering Materials PhD programme page.

In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Application deadline: 13 October 2023

Enquiries

Contact: Dr Yinglong He (yinglong.he@surrey.ac.uk).

Ref: PGR-2324-005