Current research portfolio
Find below, a summary of some of the research projects on which we are currently working...
Core research projects
This project will build fundamental tools to aid in the deployment and qualification of inspections involving ML in the processing chain through three complimentary but independent work packages. The first work package addresses the use of ML for the suppression of benign responses from component geometry in NDE measurements. The second work package concerns uncertainty in ML algorithms for NDE data. Given any input, an ML algorithm will produce an output, but the confidence and accuracy of that output are currently not well understood or quantified. We will develop techniques that can quantify uncertainty in NDE applications by giving an estimate of the error of output and flag when an algorithm is presented with data that is outside its defined domain of operation. The final work package will explore how ML algorithms for NDE can be made interpretable for end-users by producing a human-understandable description of how a result was reached. This is a key step in the path to qualification of ML-based approaches. The outcomes of this project will be fundamental developments in ML for NDE that will be generalisable to many techniques and will build a solid foundation on which future real world inspections may be developed.
Prof. Paul Wilcox, Ultrasonics and Non-Destructive Testing Group, University of Bristol; Prof. Anthony Croxford, Ultrasonics and Non-Destructive Testing Group, University of Bristol.
We will develop automated UT data analysis tools to improve the reliability of detection, sizing, and characterisation of defects which occur in high safety significance industrial plant. The project will develop capability for defects of critical industrial importance, targeting two species: Thermal Fatigue (a service-induced species) and Hydrogen cracking (a manufacturing/welding defect). Both species are known to occur and often materially impact plant availability/longevity. This project will also enable capability for other challenging defect species going forward, such as stress corrosion cracks.
Dr Stewart Haslinger, Department of Mathematical Sciences, University of Liverpool; Dr Daniel Colquitt, Applied Mathematics, University of Liverpool; Dr William Christian, Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool; Professor Jason Ralph, Department of Electrical Engineering and Electronics, University of Liverpool; Mr. Michael Wright, Signal processing, University of Liverpool; Prof. Michael Lowe, Department of Mechanical Engineering, Imperial College London; Dr Peter Huthwaite, NDE Group, Imperial College London.
Producing realistic ultrasonic data is vital for a variety of NDE applications in industry: validating methods, testing trainee inspectors, evaluating new methods being developed and generating machine learning data. Numerical methods, such as Pogo (www.pogo.software), have shown strong promise for generating physically accurate data, but suffer from this data missing physical imperfections and noise, as well as often being computationally demanding to produce in the quantities needed, despite the ability to use high-speed hardware such as GPUs. This is a notable challenge for applications such as inspection qualification where many realisations are required across the parametric space of different defects, geometrical considerations etc. This project aims to develop techniques to enable ultrasonic NDE methods to produce necessary data quickly and reliably. The project has three objectives: 1) Develop methods to combine datasets together to generate accurate data at high speed, such as inserting a defect response into another dataset. 2) Develop interpolation techniques to enable finer sampling to be produced from coarse simulations. 3) Develop suitable sampling strategies for high-dimensional parametric space, by eliminating dependent parameters and enabling global performance to be assessed with few simulations.
Dr Peter Huthwaite, Department of Mechanical Engineering, Imperial College London
SHM research often focuses on defect detection methodologies and algorithms. However, the ultimate success of the technology depends on the capability to enable reliable and cost-effective data acquisition of SHM signals in the first place. For ultrasonic SHM, the only widespread technology that is currently deployed in the field in large numbers (>10,000 units) relies on single channel ultrasonic measurements for localised thickness evaluation. These are the only systems for which cost effective acquisition systems exist. Many more advanced SHM/NDE techniques are known which utilise multiple ultrasonic measurement channels, e.g. phased array technology, for identifying additional types of defects, e.g. cracks. However, the acquisition hardware for synchronised multi-channel data acquisition is currently too costly to enable permanent installation. Sensor patches containing multi-channel ultrasonic transducers can be installed but they still require that an inspector or technician comes up to the structure for sensor interrogation. Over the last 8 years, we have researched a novel way to acquire ultrasonic signals for SHM applications using coded excitation techniques and we have recently made a major breakthrough. This breakthrough has resulted in the ability to simultaneously transmit and receive data on multiple channels using TOP-CS coded sequences with low-power hardware based around very simple micro controller units (MCUs) or field programmable gate arrays (FPGAs) [1]. To date we have successfully demonstrated many low channel count applications using this coded excitation approach, in this work we want to fully explore the multi-channel capabilities and scalability of the approach. The vision being that multi-channel acquisition systems can become as cost effective as single channel systems and single channel systems will become even more affordable.
Dr Frederic Cegla, Mr Connor Challinor, NDE Group, Mechanical Engineering Department, Imperial College London
Understanding, predicting, and controlling material properties requires quantitative characterisation of material microstructure, which is a topic of considerable importance in materials science with a wide range of applications. For example, the knowledge of material microstructural parameters is crucial for accurately estimating the lifetime of safety critical components in aerospace (jet engines and landing gears) and energy sectors (nuclear power plants), or for characterising material performance and designing new materials with specific properties. Current state-of-the-art techniques such as optical and atomic force microscopy (AFM), scanning electron microscopy (SEM), focused ion beam (FIB), X-ray tomography (XRT), Electron Back- Scattered Diffraction (EBSD) or Spatially Resolved Acoustic Spectroscopy can achieve extremely high resolution up to nanometres, but either in two dimensional (2D) cross-sections or in relatively small imaging volumes (containing a few grains in polycrystalline materials). However, in many cases the nano- or micro- level information is only needed in specific regions of interest (e.g. sites of potential defect initiation and growth), which leads to a sampling problem (size effect) if the microstructural feature occurs infrequently in the material. Often critical microstructural regions are located beneath the surface and can only be identified by three-dimensional (3D) large field of view (of order of centimetres) microstructure imaging. Ultrasound offers a promising potential for 3D imaging because of its relatively low cost, inspection speed, and portability, so it can be used on components in situ unlike other modalities (for example, X-ray CT). However, conventional ultrasonic methods for quantitative microstructural characterisation are based on single- element probe measurements to estimate average material properties, which provide important, but limited information. In contrast, ultrasonic arrays allow a vast amount of complex data to be measured. To leverage this data and turn it into actionable information, fundamentally new processing methods must be developed. The proposed project will develop a material characterisation framework based on using ultrasonic array measurements to achieve microstructural characterisation of each local material region and creating a volumetric map of local microstructural properties. This will lead to an efficient tool for quantitative 3D ultrasonic imaging, which will enable important information about material microstructure to be obtained using a rapid non-destructive procedure. Moreover, together with the array image of the material interior, it will link the local material microstructure with the geometry of the test component.
Dr A Velichko, UNDT Group, University of Bristol; Dr M Peel, Solid Mechanics Group, University of Bristol
The vision behind this proposal is to create the first, non-contact, in situ, laser ultrasound array sensor that can inspect components during manufacturing. This system will pave the way for the development and deployment of real-time, in process non-destructive evaluation (NDE). It represents a vital step towards achieving sustainable and reliable manufacturing of high value, safety-critical components. In the long term (10-year vision), the proposed system has the potential to form the basis of a feedback loop with manufacturing parameters, enabling enhanced control of the manufacturing process, in order to realise the “right first time” manufacturing concept.
Optical based techniques are best suited for in-process NDE: light can sense remotely and its small footprint suits inspection of complex shapes. However, optical based techniques are usually limited to examining the near surface. Ultrasound on the other hand can probe the inner structure of materials, but conventional ultrasonic transducers require contact and cannot withstand high temperature environments. Laser ultrasound circumvents these issues as pulsed lasers can be used remotely for ultrasonic excitation and detection of all wave modes (bulk and surface waves).
The Strathclyde team has pioneered the use of Laser Induced Phased Arrays (LIPAs) for 2D and 3D ultrasonic imaging [1–3]. In LIPAs, the array is synthesised in post processing using the Full Matrix Capture data acquisition method and applying advanced imaging algorithms such as the Total Focusing Method. The current state-of-the-art of the LIPA technology has been presented to the RCNDE community in several Technology Transfer events and industrial visits in Strathclyde. Interaction with the RCNDE community has highlighted the next key areas for the development of LIPA: fast data acquisition and in situ implementation during manufacturing. These are the challenges that the proposed project aims to address.
The overall objective of this project is to deliver a fast (<1 min per frame), remote and robotically enabled all-optical based ultrasonic inspection system for the welding process.
The primary limitation of the current LIPA technology is the mechanical scanning of the ultrasonic generation and detection laser beams, which restricts data acquisition speed. The project aims to address these limitations through two key approaches: a) innovations in data acquisition methodology, including hardware and software development and b) a holistic approach to signal processing designed to maximising the use of information in the laser ultrasound data. Finally, the project will focus on the deployment of LIPAs in situ, for welding. The robotic and welding facilities in Strathclyde will be utilised marking a significant transition of LIPAs from the lab to the manufacturing environment.
Dr T Stratoudaki, Dr C N MacLeod, Dr S G Pierce Department of Electronic & Electrical Engineering, University of Strathclyde & Dr K Tant Centre for Medical and Industrial Ultrasonics, University of Glasgow
Impact enhancement projects
The project seeks to extend ALIPA, a fast, efficient laser ultrasonics (LU) method for remote, non-destructive evaluation (NDE) and process monitoring.
that can dynamically reconfigure to suit material, geometry, and inspection demands, from lab demonstrations into real-world industrial scenarios, paving the way for technology transfer. It builds on strong academic–industrial collaboration, with promising early results, and focuses on making ALIPA a practical solution for remote ultrasonic inspection in challenging environments.
Dr T Stratoudaki Department of Electronic & Electrical Engineering, University of Strathclyde
Technology Transfer & Feasibility Studies
Time-of-Flight Diffraction (TOFD) is a popular and scientifically-sound technique for the inspection of defects and is used routinely in multiple high-integrity sectors. The most common use case for TOFD is crack and lack-of-fusion detection in welds, due to its high sensitivity to near vertical defect indications. Standard practice dictates the use of single-element probes, often requiring multiple scans to cover a region of interest and comply with international inspection guidelines (ISO 10863 [1]). This work seeks to improve the efficiency of TOFD inspection by utilising the flexibility of ultrasonic phased array probes to improve inspection speed and efficiency. Through manipulation of array phase delays, total inspection time and effort could be reduced by compounding multiple TOFD scans into a single inspection. This opens up the possibility of significantly reducing inspection time while maintaining or even improving inspection quality – enabling faster defect detection, more comprehensive weld coverage, and reduced operational costs. Eventually, this could be used in tandem with standard phased array testing to improve detectability across a range of flaws.
Dr Ewan Nicolson, Department of Electronic & Electrical Engineering, University of Strathclyde
In comparison to traditional piezoelectric transducers used
in non-destructive testing (NDT), laser ultrasound (LU) techniques have various
advantages, such as non-contact operation, no requirement for liquid coupling
agents, remote inspection capabilities, and the ability to handle complex
geometries and harsh environments [[1-[2]. More recently, the laser induced
phased arrays (LIPAs) have been developed where the generation and detection
positions are independently controlled so signals can be acquired from any
possible combination of transmitting and receiving points. This enables full
matrix capture (FMC) array datasets to be acquired using laser ultrasound,
which can be used to form ultrasound images using, for example, the total
focusing method (TFM) [[3]. However, the noise level in signals obtained from
both LU and LIPAs is higher compared to those from piezoelectric transducers or
ultrasonic arrays, requiring excessive signal averaging and resulting longer
data acquisition times to suppress it. This constraint limits their adoption in
industry. In this project, we will explore the modalities of LIPA
configurations integrated with signal modulation and diffuse correlation
techniques to increase measurement efficiency and adaptability without
compromising the signal-to-noise ratio for defect detection, characterization
and material characterization. The project will focus on investigating the
feasibility of using various non-contact techniques to generate modulated
ultrasound, including modulated EMATs and modulated laser generators,
integrated with the diffuse correlation techniques to efficiently reconstruct
high-quality FMC array datasets.
Dr Jie Zhang, University of Bristol
Current Robotic Non-Destructive Testing (NDT) systems are predominantly based on the “Part-to[1]Process” notion,
where the part is delivered and precisely positioned within an inspection cell. They also require dedicated floor spaces bounded by safety fences and lack the
flexibility to accommodate a diverse range of components. The complex nature of high-value components exacerbates the above challenges due to their (i)
intricate geometries, (ii) as-built tolerances, (iii) unavailability or inaccessibility of precise CAD data, (e.g., remanufactured parts), (iv)high-mix low-volume production nature and (v) flexible automation demands associated with future smart factories.
Dr Randika Wathavana Vithanage, Department of Electronic & Electrical Engineering, University of Strathclyde
Recently Completed Projects
Current NDE techniques for composites are mostly based on techniques originally used on metallic structures. In-order to increase the usefulness of NDE data, new techniques are required to move beyond simple location and extent estimation towards structural prognosis. Strain-based defect assessments have the potential to be a valuable tool for assessing structural components and obtaining predictions of future performance. The aim of the project is to demonstrate the capability of strain-based defect assessments for predicting the degradation of defective composite components.
Dr William Christian, Structural Materials and Mechanics research group, University of Liverpool
Radiographic tomography data does not work well on many realistic AM components, while traditional image processing tools often require significant operator intervention. Advanced deep learning tools are now becoming a promising alternative that can address both issues, but they rely on the availability of large sets of training data, which is not generally available in AM component inspection. To develop deep learning based automated procedure that can work in a wide range of realistic settings, the efficient simulation of domain specific training data is thus crucial to generate the required quantity of training data. This one year project will investigate efficient simulation approaches to generate realistic tomographic data, looking at how this can be used to train and verify defect detection and classification models.
Dr Thomas Blumensath, µ-VIS X-ray Imaging Centre, University of Southampton
This four year project from the University of Bristol aims to develop a general and scalable methodology for automated NDE data analysis based on data science. With a need to analyse ever-greater volumes of complex data from multiple sensing modalities, it uses the availability of computational power to synthesise large (order 10,000-100,000) sets of realistic training data to make a supervised learning approach to NDE data analysis tractable for the first time.
Prof. Paul Wilcox, Ultrasonics and Non-Destructive Testing Group, University of Bristol
The overall aim of this project is to research approaches for simultaneous thermal and microstructure compensation strategies for in-process additive and fusion welding imaging and inspection. It addresses the challenge posed by in-process ultrasonic volumetric imaging of the upper layers of an additive or welded build; namely the effect on wave velocity from thermal gradients and varying microstructures across the component due to build geometry, density, features, arc power and post-arc time. Ultimately, if not accounted for, these variations can give rise to significant incorrect defect size and position, in the order of millimetres in common metallic materials, and hinder the accuracy and ultimate potential of in-process inspection for real-time defect-free process control.
Dr C.N. MacLeod, Dr K. Tant, Centre for Ultrasonic Engineering, University of Strathclyde
The current industrial needs for 3D, volumetric ultrasonic imaging are mainly covered through the use of transducers and transducer arrays. However, transducers have certain drawbacks and limitations: a) it is a contact technique, b) they require couplant, c) they have a considerable size/weight/footprint which may be prohibitive in places with restricted access and e) their delicate electrical connections and packaging may not withstand extreme environments. In short, they are not suitable for remote imaging. The existing project (Remote, 3D ultrasonic imaging in extreme environments using 2D laser induced phased arrays) combines the remote, non-contact nature of laser ultrasonics with the modern paradigms of FMC and TFM in ultrasonics in order to synthesise remote, couplant-free 2D Laser Induced Phased Arrays (2D LIPA) for volumetric, ultrasonic imaging. The aim is to apply then in extreme environments, for in-line process monitoring or in-service volumetric inspection. The aim of the impact enhancement activities is to demonstrate the industrial relevance of 2D LIPAs. We will collaborate with RCNDE industrial members to identify, design and build samples with surface finish, material and geometry that are challenging to existing NDE methods and interesting to industrial applications. At the end of the impact enhancement (IE) grant we want our system to be able to perform remote volumetric imaging of complex components of industry relevant finish and material. Our aim from all these activities is to boost our chances towards technology transfer of 2D LIPAs and the IE funding will provide the means to creating new industrial networking opportunities. Finally, one of the key outputs will be to identify strengths and limitations of the technique, paving the way for attaining higher TRL levels.
Dr Theodosia Stratoudaki, Department of Electronic & Electrical Engineering, University of Strathclyde; Dr Geo Davis, Centre for Ultrasonic Engineering, University of Strathclyde
In this proposed research work the benefit of the inspection/monitoring mix for different scenarios of degradation and measurement capability will be explored. The amount by which the intervals can be stretched relies on the measurement errors in both the inspection and monitoring method and the spatial and temporal characteristics of the degradation. Therefore, the key challenge to address is to quantify the effect of measurement errors in the process so that probability of detecting and tracking damage can be quantified and spatial coverage and repeat scan intervals can be optimised. In order to do this we will first build a statistical model that simulates the spatio-temporal degradation, based on Kuniewski’s work [3]. We will use this model as ground truth and perturb it to simulate measurements containing errors that are expected in real measurements, e.g. spatial offsets and biases and random noise. Similarly, we will use estimates from experiments and previous work to impose trending errors on the data. Finally, we will use the simulated measurement results to analyse the performance of a pure inspection strategy, or a pure monitoring strategy and mixed strategies and inspection intervals that should be used to get the best results.
[3] S. Kuniewski, “Sampling inspection for the evaluation of time-dependent reliability of deteriorating structures,” Proceedings of the European Safety and Reliability Conference 2007, ESREL 2007 – Risk, Reliability and Societal Safety, vol. 1, Jan. 2007.Dr. Frederic Cegla, NDE group, Mechanical Engineering Department, Imperial College London
Most engineering metals are microscopically polycrystalline, and their manufacturing processes result in the ever-present grain microstructures in the finished products. These include preferred crystallographic orientations (texture), and the so-called macrozones in titanium alloys. These microstructures have been found to be the culprit of many catastrophic failures (e.g. airplane motor explosions), but they are very difficult to detect during the manufacturing process or on the finished components. This project aims to develop an ultrasonic method for the characterisation of the microstructures. It is based on a recent advancement of an ultrasonic measurement of volumetric texture, and will focus on the following areas of work: 1) Automation of the immersion data acquisition equipment. This will expand the existing IP and significantly reduce the data acquisition time in the lab. 2) Physical understanding of shear wave attenuation, which can potentially enable the estimation of statistical geometries in 3D using the same immersion setup for texture measurement. 3) Interpretation of bulk attenuation and backscattering data to obtain microstructure information from normal-incident scans. 4) Exploration of the diffuse wave field methods to apply the characterisation capabilities on complex geometries.
Dr Bo Lan, Non-Destructive Evaluation Group, Department of Mechanical Engineering, Imperial College London
