University: Strathclyde Supervisor: Dr Charles MacLeod
Sponsor: Peak NDT
EngD Project: In-Process Adaptive NDT for Fast & Flexible HVM
This project aims to improve the flexibility, accuracy and speed of in-process High Value Manufacturing (HVM) robotic inspection and control operations, with a focus on three key challenging applications: arc welding, wire and arc additive manufacturing (WAAM) and complex carbon fibre composites.
University: Bristol Supervisor: Professor Paul Wilcox Sponsor: Rolls Royce
EngD Project: Automated Sentencing for Complex Shaped CFRP Components
Carbon fibre material is increasingly being considered for aero-engine components. Such components are being designed to withstand high loads and to have more complex shapes, making inspection for material integrity both more important and more difficult. New analysis techniques are required and are being developed for defects such as porosity. However, such techniques create large data sets that become time consuming and difficult, or impossible, to interpret by a human inspector.
The aims of the project will include: 1. Investigation of new quantitative data analysis techniques for porosity detection in CFRP. 2. Development of data fusion methods to allow extraction and presentation of information from multiple data sources. 3. Automated interpretation of 3D data.
University: Imperial College London Supervisor: Dr Peter Huthwaite Sponsor: Guided Ultrasonics Limited (GUL)
EngD Project: Measurement Interpretation for Guided Wave Testing
The ongoing development of Guided Wave Testing of piplelines is increasingly generating large amounts of measurement data that needs to be interpreted. This includes change detection from repeat measurements acquired from permanently-installed transducer equipment, the automation of the interpretation of routine measurements, and the assessment of complex signals to extract image or critical parameter information. The pressure to develop software tools to aid these tasks is amplified by the increasing availability of the large quantities of raw data via cloud communications. This project will work on developing methodologies and implementing these in software tools to address these needs.
University: Bristol Supervisor: Professor Anthony Croxford
PhD Project: Autonomous NDT
The aims of my project and to work closely with University spin out company ‘Inductosense‘, who developed innovative wireless, battery free, and permanently installed sensors for non-destructive testing. I will develop a robotic platform that ultimately aims to deploy sensors and take measurements autonomously with given locational information of where the sensors are permanently installed, which hugely increases the accessibility and reduces the cost of operation.
To achieve the goal, accurate positioning and perfect alignment between the measurement probe and sensors are required.
Zubeir Ebrahim Saib
University: Bristol Supervisors: Professor Bruce Drinkwater & Professor Anthony Croxford
PhD Project: Early detection and characterisation of defects using nonlinear ultrasound
One of the grand challenges in non-destructive testing (NDT) is the measurement of the remaining life of a structure.
Up to now, most NDT methods aims to detect and characterise relatively large defects which occur at the end of the life of a structure using linear ultrasound. Potential use of nonlinear ultrasound has been demonstrated in the literature to be sensitive to early formation of defects, such as microcracks. Different techniques exist, namely second harmonic generation and diffuse field. However, whilst these new nonlinear imaging or defect characterisation techniques have shown promising results in laboratory experiment, they have not yet reached the sensitivity to image the build-up of material nonlinearity due to aging, or other effects such as thermal or plastic deformation.
This project aims to develop the required modelling tool to fully understand the measurement scenario and devise experiments to extract material nonlinearity to predict failure at an earlier stage.