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Cohort 4
Qiming Kong
University: University of Bristol
Supervisors: Professor Bruce Drinkwater and Dr Alexander Velichko PHD project: Defect characterization of ultrasound images using image processing technology
With the gradual maturity of ultrasonic imaging technology, the characterization of defects through ultrasonic images has gradually emerged. However, the development of ultrasound image processing has been restricted due to the scattering problem of ultrasound and the lack of good imaging data. The aim of this project is to devise a more automated method for characterizing defects. By improving the existing imaging algorithms to reduce the interference caused by scattering, and designing a sufficient amount of simulated data for image processing and machine learning experiments. And through the above method to design a better ultrasonic image processing algorithm.
Brandon Mills
University: University of Strathclyde
Supervisor: Dr Yashar Javadi Project Title: Non-destructive evaluation of residual stress using the collaborative robot
The basis of this project is to non-destructively evaluate the residual stress induced by welding, and then to integrate this method using the collaborative robot to allow for automated and possibly in process testing.
Atul Sharma
University: University of Bristol
Supervisors: Dr. Robert Hughes (Bristol) and Prof. Anthony Peyton (Manchester) Industrial Sponsor: Ether NDE
Eng.D Project: Eddy-Current inspection of complex geometries in carbon fibre composites
Carbon fibre composites are used in many engineering industries, including aerospace, automotive, and the energy sector. Non-contact inductive (eddy-current) sensors have been shown to detect damage and manufacturing flaws in carbon fibre structures, however, the complex electromagnetic interaction with these multi-layered anisotropic fibrous structures is poorly understood, preventing accurate characterisation of critical mechanical properties such as fibre volume fraction. This research aims to develop finite element COMSOL models based on detailed multi-scale experimental characterisation (employing, micro-CT imaging and conductivity coupon testing) of the statistical variation in electro-structural properties of carbon fibre composite materials to more accurately model the eddy-current measurement of composite structures. The results of the study will be used to aid in the inversion of critical material and structural properties of industrial components, as well as to optimise probe designs and establish a database of highly-characterised material properties to assist in training future machine-learning inversion algorithms.
James An
University: University of Bristol
Supervisors: Dr. Jie Zhang, Dr. Alexander Velichko and Dr. Nicolas Larrosa Project title: Defect Characterisation Using Ultrasonic Arrays
Over the last decades, there has been a rapid increase of using ultrasound arrays to detect and characterize defects. Particularly, post-processing imaging algorithms such as Total Focusing Method (TFM) can be used on a full set of captured matrix data (FMC) to perform high-resolution imaging. However, due to the diffraction of waves, the imaging resolution of ultrasound array is limited to around a wavelength of the ultrasound in use, and this leaves high uncertainty to small defect characterization from its ultrasound image. The scattering behavior from small defects can be extracted (scattering matrix) and used to characterise it. However, the accuracy of characterising procedure is affected by surrounding noise and defect surface condition such as roughness and asperities. This limits the real application of defect characterization using ultrasonic arrays in practice (within 0.1 wavelength accuracy). This project will look to develop a method to characterise defects more accurately by suppressing the effect from surrounding noise and defect surface condition.
Yanghao Wu
University: University of Bristol
Supervisors: Professor Paul Wilcox and Professor Anthony CroxfordPHD project: Generic small robot for replacing manual ultrasonic inspection tasks
The aim of this project is to develop techniques using generic robots to automate some standard Ultrasonic Non-Destructive Evaluation Measurements. The most potential application is to detect fatigue cracks at fastener holes of aircraft metallic components. In this project, both 1D and 2D ultrasound array will be used to characterize the different potential defects. Also different imaging method will be investigated. Finally the most optimized probes and imaging methods will be selected for the automatic detection based on simulation and experiment.
Cohort 3
Matthew Riding
University: University of Strathclyde
Supervisor: Dr Theodosia Stratoudaki & Dr Charles MacLeod Project: Novel schemes of laser ultrasound deployment and data analysis to solve industrially relevant NDE challenges
Laboratory experiments with laser ultrasonic testing (laser UT) have demonstrated the ability to detect and characterise manufactured defects within solid components in fully non-contact schemes. Demonstrations of advanced laser UT techniques, such as laser-induced phased array inspection, have until now mostly been performed on simple samples with favourable acoustic properties and exaggerated, artificial defects. Whilst such simple test samples are ideal for experiments focussing on fundamental technique development, a wider range of sample materials, geometries, defects and inspection problems must be explored in order for advanced laser UT techniques to mature further. This project therefore aims to perform advanced laser UT experiments on novel samples, identify issues limiting the information obtained and then apply novel approaches to both experimental configuration and data analysis to demonstrate the applicability of this technology to industrially relevant NDE challenges.
Gala Dalgety
University: University of Warwick
Supervisor: Dr Rachel Edwards Project: Automated Implementation of EMAT Inspection
This project essentially looks into the intelligent operation of robots with non-destructive testing (NDT) capabilities. This will include building on previous developments in making miniaturised electromagnetic acoustic transducers (EMATs) capable of generating and detecting shear horizontal (SH) waves. Advantages of this include improving spatial resolution, and making implementation onto a robotic platform simpler, particularly for inspection of ferritic steel. However, the signal to noise ratio (SNR) of these techniques are smaller than other ultrasonic alternatives and it will be essential to explore how to improve signal analysis. One of the ways to improve SNR will be to look into focusing and phasing the SH waves and taking advantage of any directivity the EMATs have. Other solutions to improve SNR to be explored include utilising other wavemodes and sensing techniques and performing data fusion, looking at robot scanning techniques to give high probablilities of defect detection, and the use of multiple scans to find if there is a defect in the area and then rescan the areas with a high probability of a defect.
Martha Turvey
University: University of Warwick
Supervisor: Rachel Edwards Project: Liquid crystal sensors for ultrasound detection
Liquid crystals can be used to make a passive detection sensor for ultrasound, providing a visual image of the propagation of ultrasound through the sample. Demonstration of the sensor has focused on analysis of the resonant modes of a piezoelectric transducer, and visualisation of longitudinal waves passing through a sample. Although the technology is at a low technology readiness level (TRL), several potential avenues are ready for exploitation. This project will identify the key areas for development, look at potential applications, and develop the sensors such that they can be used for non-destructive testing (NDT).
Shaun McKnight
University: Strathclyde Supervisor: Professor Gareth Pierce
Phd Project: Machine Learning for NDE Inspection
Ultrasonic NDT methods are critical in the testing process of manufacturing for composite aerospace components. There are ambitions to automate this process, however the requirement for manual interpretation of NDT results remains a bottleneck in the testing. My project aims to address this challenge by developing Machine Learning algorithms to allow for automatic interpretation of results. This project hopes to integrate with other non-destructive testing projects with the overall aim of automating the complete testing process.
Mo Zubayr
University: Nottingham Supervisor: Adam Clare
Phd Project: Unveiling materials with robotic electrochemistry
The aim is to automate the testing of metals using an electrochemical jet to both make measurements and to machine the surface using industrial robotics. Electrochemical jet machining can be used to deposit, remove, and pattern material on the surface of a wide range of metals. The size of the jet as well as other parameters such as current can be used to control the surface area of material removal and addition. One of the objectives is to make measurements during machining and adjust the process accordingly. Measurements made can tell us how much material was removed as well as the roughness for example without having a dedicated process to determine such properties. Usually, such analysis would be done after machining and is usually outsourced to a specialist institution with specialized equipment and training individuals performing the analysis. The main objective will be to research and develop a system that can be integrated into a manufacturing process and perform analysis and adjustment autonomously.
Martin McInnes
University: Strathclyde Supervisor: Dr Ehsan Mohseni
PhD project: Automated in-process inspection of additively manufactured components using ultrasonic testing and eddy currents
The aim is to automate eddy currents and ultrasonic testing using industrial robots. Throughout the project, the plan is to simulate, design and build electromagnetic and ultrasonic sensors in which they will be integrated with automated systems and their performance evaluated through the inspection of aeroplane composite materials.
Lily Tu
University: Bristol Project Supervisor: Professor Paul Wilcox
Phd Project: Laser induced 2D phased array for composite material inspection
Laser induced phased arrays (LIPAs) have several advantages over traditional piezoelectric ultrasound arrays in terms of their non-contact, couplant free nature, and their ability to change array geometry during an inspection as the element positions are not fixed. The proposed research will design a 2D LIPA for inspection of defects in composite materials using simulated data and implement the array design in experiments. The successful outcome of the research will demonstrate the suitability of using LIPA as 2D array in improving defect inspection and characterisation in composite material.
Laurence Clarkson
University: Imperial Supervisors: Dr Frederic Celga
Phd Project : Estimation of internal pipe wall temperature
There is concern that rapid temperature changes in the primary cooling circuit of reactors can induce thermal fatigue that can be detrimental to the overall life of components in the primary circuit. It is therefore desirable to monitor the number and magnitude of cross wall temperature changes in these components so that one can better estimate the magnitude and number of stress cycles that a component experience to predict the overall life of the components. This project aims to build on the outcomes of a recent feasibility study to enable real-time monitoring of temperature wall gradients in primary circuit components under realistic conditions (up to 350°C).
Andre Lello
University: Imperial Supervisors: Dr Bo Lan and Professor Mike Lowe
Phd Project: Characterisation grain microstructures in metals using ultrasound
This project aims to characterise critical microstructures within engineering metals using ultrasound. Most metals are polycrystalline, and their critical performances are determined by the grain microstructures. The NDE group at Imperial College has developed a breakthrough methodology to extract volumetric texture from the speeds of compressional ultrasound. It has been validated on a conventional water-bath system, which, in addition, has produced experimental evidences that the shear waves propagating through a metal sample are strongly affected by the microstructures. The amplitudes of the shear waves correlate with the grain sizes in the propagation direction, and they vary significantly when the waves propagate in different directions in 3D, which indicates the exciting possibility to simultaneously evaluate the texture and the volumetric morphologies of the microstructures non-destructively from the same ultrasound setup. This doctorate project aims to understand the physics of the wave behaviours and develop the method for industrial application.
Alex Towlson
University: Bristol Supervisors: Professor Anthony Croxford and Professor Bruce Drinkwater
PhD Project: Robotic pipe inspection with ultrasonic sensor arrays
The aim of this project is to develop an ultrasonic sensor system and associated data analysis tools to extract information from the interior of a buried pipes, to detect and characterise defects such as cracks and corrosion. Such a system is required to operate in a variety of conditions, including pressurized and partially filled pipes with flow. In order to be easily deployed, it is important for the device to be compact and energy efficient, so that it can be mounted on-board an autonomous robot.
