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