The Through-life Engineering Services Institute hosts 25-30 research students who work on real problems in collaboration with industry. Current research projects, across the full range of the Institute’s interests, include in-depth technical investigations and validations, business applications, and human factors in systems, for example:
- Sensing techniques for manufacturing process degradation, including thermal emissions, vibration and acoustics;
- Signal processing for extracting information from data, analytics and visualisation;
- Repair technologies for a range of metal and composites;
- Management of cost prediction, obsolescence, and solutions for legacy equipment;
- Augmented and virtual reality.
Many of our PhDs have full or part industry funding, and we also research a wide range of topics proposed directly by applicants.
View all Manufacturing PhD opportunities.
Alternatively, Cranfield offers you the opportunity to study with us on a self-funded basis in the following research areas:
Business modelling to evaluate predictive maintenance benefits for commercial aircraft fleet level total ownership cost
Predictive maintenance is promising a step change to commercial aircraft operations. Benefits realisation depends on the business and operations approach of the airlines and leasing companies. This research aims to complete a model to simulate the dynamics of fleet total cost of ownership, part of the Cranfield Digital Aviation initiative.
Lead academic: Dr Ip-Shing Fan
Information modelling to support paperless operations for commercial aircraft through life maintenance and support
Maintenance of commercial aircraft involves a lot of papers, imposed by authorities and commercial stakeholders. This research works with industrial partners to create and validate an ontology model for aircraft through life support, and develop pre-competitive commercial demonstrations as part of the Cranfield Digital Aviation initiative.
Lead academic: Dr Ip-Shing Fan
Corrosion-sensitive multiscale fatigue modelling
Study the effects of corrosion-induced changes in composition on fatigue damage in metallic materials. We will employ multiscale models to understand the role of compositional changes on plastic deformation and inform fatigue prognosis approaches.
Lead academic: Dr Gustavo M. Castelluccio
Multiscale modelling of metallic materials after overloads
Study the mechanical response of metals after overloads by developing physics-based models informed with experiments across scales. We seek to explain why some overloads may have no noticeable effect in metals while other events can profoundly affect their mechanical response.
Lead academic: Dr Gustavo M. Castelluccio
