Join our vibrant and diverse research community, applications are now open for 2020/21 intake of fully-funded PhDs in a wide range of Metrology topics.
You will receive excellent support and have access to impressive facilities.
The Hub has a number of funded PhD research projects listed below. To find out more and how to apply, please click on the project.
Development of Bio functionalized surfaces using Additive Printing of Materials
Under the Skin of Ultra-Precision Manufacturing.
Manufacturing control through online surface prediction.
Self-calibration of embedded strain sensors for traceable measurement of structure.
Towards metrology systems on a chip using photonic integration.
Application of advanced measurement techniques to tool mark investigation.
Defining and evaluating geometric dimensioning and tolerances for topology optimised additive manufactured components.
Determining the effect of cartridge variability on ballistic toolmark investigation.
Investigating x-ray computed tomography for the characterisation of critical additively manufactured aerospace components.
Intelligent filtration for complex freeform structured surface.
A smart geometric information system.
Sparse data structure for computed tomography measurements.
Development of new generation surface metrology software.
Development of a compact broadband interferometer.
Development of integrated self-aware sensors for manufacturing.
Data-driven intelligent micro and nanomanufacturing technologies for functional surfaces.
Design and manufacture of freeform functional surfaces in novel optical materials.
Development of a virtual integrated ultra-precision manufacturing and metrology system for advanced freeform surfaces.
Manufacturing control through online surface prediction
This project is looking for a cost-effect solution to implement online surface prediction and stability monitoring based on IIOT and edge computing with traditional Digital Signal Processing (DSP).
Self-calibration of embedded strain sensors for traceable measurement of structure.
In this project, a newly developed sensor, which uses phase-resolved interferometry to measure the elongation of an optical fibre, will be used to measure the structural distortion of mechanical systems such as the main elements of advanced machinery.
Towards metrology systems on a chip using photonic integration.
This PhD project will investigate routes to the creation of optical metrology systems-on-a-chip, where photonic integration will be used to develop a miniaturised broadband interferometry system comprising light sources, detectors and other necessary sub-components, to achieve low-cost miniaturised sensors for the measurement of surface topography, layer thickness and position/displacement.
Application of advanced measurement techniques to tool mark investigation.
This project will focus on: • Determining the characteristics of tool marks and the best practice for measurement of the topography • Exploring casting techniques used when the tool mark cannot be transported to the lab (i.e. doorframes) and the effect of casting on the fidelity of the replica • Applying correlation techniques to the areal surface • Analysing possible error rates in the investigation.
Defining and evaluating geometric dimensioning and tolerances for topology optimised additive manufactured components.
This PhD project will look at evaluating and adapting current approaches to GD&T to create best practice guidelines in terms of both defining and inspecting topology optimised components.
Determining the effect of cartridge variability on ballistic toolmark investigation.
This project will focus on determining the physical and measurable differences in toolmark topography as a result of a number of variables, and the effect on correlation techniques.
Investigating x-ray computed tomography for the characterisation of critical additively manufactured aerospace components.
This project will include the use of CT to evaluate a selection of aerospace components, to produce traceable measurement test artefacts designed specifically to aid evaluation of the measurement uncertainty for each component. Work will be performed to generate MTBF times for specific components.
Sparse data structure for computed tomography measurements
The aim of the project is to use a sparse volume data structure to convert a dense volume into a lighter representation. Using a sparse data structure allows faster filtering and segmentation processes.
Development of new generation surface metrology software.
This PhD project is to develop next-generation geometry/surface metrology software for the characterisation of new types of freeform and structured surfaces.
Development of integrated self-aware sensors for manufacturing.
This aim of this project is to develop fundamental concepts behind a ‘self-aware’ sensor that can holistically evaluate the full gamut of information contained within an acquired signal in order to determine not only the primary information e.g. height/distance but also to track changes in other secondary parameters such as alignment, calibration status, environmental/measurand variation.
Data-driven intelligent micro and nanomanufacturing technologies for functional surfaces.
The aim of this PhD research project is to develop data-driven intelligent micro and nanomanufacturing technologies for high value-added functional surfaces.
Development of a virtual integrated ultra-precision manufacturing and metrology system for advanced freeform surfaces.
The aim of this PhD project is the development of a virtual integrated manufacturing system for freeform surfaces based on our current freeform CAD/CAM framework. The virtual system will be developed with functional modules covering surface generation, toolpath planning, machining stability analysis and surface profile predication.
This studentship concerns research in advanced robotic manufacture of ultra-precision surfaces in glassy materials, required for numerous applications in science, medicine, and industry. Fine surface processing involves “rubbing” rather than cutting, is of limited predictability, and still poorly understood. The studentship is associated with a major new EPSRC-funded project to deepen that understanding, by exploring challenging multi-physics, multi-scale science at the interface between computational fluid dynamics and molecular dynamics. This will be informed by controlled experiments with extensive process-monitoring, with modelling reinforced by machine learning. The the end-game is to use real-time process-monitoring to keep processes ‘on track’, reducing manufacturing cost, time and defect rate.
