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Automated Aircraft Inspections using Robotics PhD

Automated Aircraft Inspections using Robotics PhD

Key information

Qualification type

PhD/DPhil - Doctor of Philosophy

Subject areas

Computer Cybernetics Aircraft

Course type


Course Summary

This course aims to prove the feasibility of using automation technology in aircraft maintenance inspection as well as to develop a technology demonstrator for future industrialisation and upscaling. Airframe inspection is an integral part of postflight and routine maintenance checks for certifying the airworthiness of an aircraft, but manual inspection is typically labour intensive, costly, and requires qualified aircraft engineers who can spot tell-tale signs of irregularities. State-of-the-art automated inspection technology is primarily used in manufacturing systems with controlled lighting and other environmental parameters. A novel automated inspection system could be a promising solution in terms of reducing operating cost, improve the consistency of inspection, improve traceability, and upskilling engineers.

This research proposes to develop an automated aircraft inspection system that could automatically scan the exterior of an aircraft and identify any surface and geometrical anomalies on the main structure. This research will develop a bespoke end-effector that combines the use of optical, laser and ultrasonic scanners, and analytics software to fuse the three different input for defects identification. The developed end-effector will be integrated with an industrial robot for manipulating the position and orientation of the sensors as well as to enable access to awkward locations and confined space in the aerostructure. The student will be based at Cranfield University in the Integrated Vehicle Health Management (IVHM) Centre, which is part of the Manufacturing Department. The student will have the opportunity to work with experts in the diagnostics, prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield.

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This Network brings together both research students and staff, providing a platform for our researchers to share ideas, identify opportunities for collaboration and create smaller communities of practice. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

Different course options

Full time | Cranfield University | 3 years | MAR

Study mode

Full time


3 years

Start date


Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


Entry requirements

Students need to have at least a 2:1 Honours degree or international equivalent, in engineering, mathematics, or related subject. The ideal candidate should have some understanding of the areas of machine learning and/or algorithms for signal analysis, along with a desire to work in this exciting area. The candidate should be self-motivated, and have good communication skills for regular interaction with other stakeholders.