Nina Sweeny
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.
Nikolay Pilashev
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.
Mikolaj Mroszczak
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.
Yangjie Chen
University: Bristol
Superivsor: 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
Supervisor: Professor Bruce Drinkwater and 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 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.
Aligned Students:
Jenny Jobling
University: Imperial
Supervisor: Dr Bo Lan and Professor Mike Lowe
Sponsor: Rolls Royce
EngD Project: Phase Characterisation of Nickel-Based Superalloys using Ultrasound
Ultrasonic methods are widely used in NDE for defect detection, but there has been increasing research in using ultrasound for bulk material property characterisation, particularly for aerospace engine materials. Techniques have been successfully applied for use with titanium alloys, and my research will focus on how these can be further developed for phase characterisation of nickel superalloys, which are widely used for high temperature regions of aerospace engines.
Richard Eager
ICASE PhD Student
University: Imperial
Supervisor: Dr Peter Huthwaite
Industrial Sponsor: Guided Ultrasonics Limited (GUL)
PhD Project: Model-based Inversion of Guided Waves in Pipes
My project will explore full wave inversion using finite element modelling, focusing on guided waves. This will be a model building tool that will allow for the quantitative detection and imaging of defects when given a measured signal, while maintaining the long range sensing benefits of guided waves.
Alasdair Regan
ICASE PhD Student
University: Manchester
Supervisor: Professor Anthony Peyton
Sponsor: Tenaris
PhD Project: Electromagnetic Non-Destructive Testing for Inspecting the Microstructure of High Performance Ferritic Steels
New techniques to measure the microstructure of a material in a non-contact non-destructive fashion can lead to a dramatic improvement in the understanding of the material and its behaviour during processing and in-service, and an ability to control or predict the material properties. This project will consider advanced magnetic techniques for inspecting the microstructure of high value ferritic steels during manufacture and in service, focussing on applications of strategic importance to industry. The key aims are to establish robust relationships between microstructure and the magnetic properties and to devise sensors which exploit these relationships for use in the field.
Rory Mansell
PhD Student
University: Manchester
Supervisor: Professor Timothy Coates and Dr Martin Fergie
PhD Project: Machine learning to locate defects in ultrasonic inspection images
It is important that manufactured components are inspected to identify defects which may cause early failure, particularly in safety critical systems. Non-destructive techniques, such as ultra-sound, are used regularly to be able to see below the surface to identify hidden defects. This project aims to develop automatic techniques to help identify the defects. The student will combine image analysis and machine learning methods to build a system that can reliably distinguish between normal parts and regions with abnormalities. This project will investigate the application of novel analysis techniques to ultrasonic NDE inspection, aiming to support the analysis of phased-array or Time-of-Flight-Diffraction images.
The project is actively supported by BAE Systems Maritime who deploy these techniques in a large scale manufacturing environment. The benefits of a robust automated analysis process would be very significant and could potentially reduce the inspection cost and duration for large scale welded structures across many industrial sectors.
Euan Duernberger
PhD Student
University: Strathclyde
Supervisor: Charles MacLeod
PhD Project: Advanced non-destructive testing of blade manufacturing defects
Wind turbine blades are one of the most costly and complicated components of the wind turbine. Now approaching 100m in length, they are manufactured using carbon and glass fibre reinforced polymer composites. Non-Destructive Testing (NDT) methods are utilized to identify any potential defects so to reduce potential asset outages, operational maintenance costs and extend lifetimes. Ultrasonic testing techniques are a promising NDT method for blade inspection, due to volumetric inspection capability, but current uses are limited due to complexities with the data/image analysis typically performed by trained experts. The aerospace industry has pioneered investigation into contactless scanning methods for complex geometry inspection, coupled with detection algorithms and machine-learning techniques. Investigation and optimisation of these methods to wind turbine blades will enable more efficient and cost-effective inspections resulting in overall benefits for clean energy production. A review of current ultrasonic NDT techniques for composite materials will be conducted and the most applicable for the wind industry identified. These will be tested on small blade samples, provided by industrial partner Siemens Gamesa Renewable Energy. Following on from this, processes are to be upscaled with the aim of developing mature and fully automated inspection, and image analysis, techniques used in large-scale blade manufacturing.
Ross McMillan
FUSE PhD Student
University: Strathclyde
Supervisor: Dr Gordon Dobie
Project: Utilise a Multi-Agent System and EMAT’s to carry out thickness measurements and defect detection within ferromagnetic structures
Alan Keenan
PhD Student
University: Strathclyde
Supervisor: Dr Theodosia Stratoudaki
PhD Project: Laser Generated and Detected Ultrasound for NDT
Peter Lukacs
PhD Student
University: Strathclyde
Supervisor: Dr Theodosia Stratoudaki
PhD Project: Volumetric imaging for Laser Induced Phased Arrays (LIPA)
Alistair Poole
PhD Student
University: Strathclyde
Supervisor: Professor Gareth Pierce
Sponsor: TWI
PhD Project: Advanced End Effector System for Robotic Inspection Systems
