Applications are invited for a PhD degree in the area of Transport Systems. The research would focus on Autonomous Systems Inspection of Aerospace Structures using AI.
Background
Aerospace industries are required by regulatory bodies to inspect aircraft before and during service as part of establishing aircraft serviceability. Aircraft are increasingly being made from composite structures due to the lower weight and lower fuel requirements. Airlines require minimised operating costs to be financially viable and competitive. Non-Destructive Testing (NDT) inspection techniques are used to inspect aircraft for defects. Defects identified, are measured and located against stringer/ station numbers, and then cross-checked against manufacturers tolerance requirements to assess the need for fix/no-fix. With this research, you will contribute to moving beyond conventional NDT approaches and concentrate on enhanced NDT inspection techniques for automated damage assessment. This way you will accurately identify, localise and measure defects, in real-time and at the same time minimize the time of inspection, reduce human involvement and thus operational costs.
Objectives
The project will focus on delivering an advanced software inspection tool without the need for a human to be involved in the inspection. The autonomous inspection solution developed through this project using artificial intelligence approaches will accurately identify defects and make credible “go/no-go” decisions.
The aims will include:
- Record of detecting damage, as a function of distance, damage type, damage size, and/or damage depth using advanced imaging approaches.
- Develop a software tool that allows for the accurate identification of defects during aircraft that has the ability to identify defects even with a limited amount of data.
- Increase the capability to detect small structures/defects, by investigating image-enhancing technologies and implementing an automated process flow in order to achieve full ADR (Automated Defect Recognition).
- Classify the detected defects, as well as categorise the types of defects using different machine learning algorithms (built and tested).
- Develop an advanced diagnostic defects detection algorithm for autonomous systems inspections of aircraft structures.
Aim
This PhD research aims to deliver an innovative scientific and technological research study, in order to address a novel, original and long-term vision concept, concerning a developed methodology aiming to the prompt and automated damage diagnosis and characterization of aerospace structures. The breakthrough aimed to be achieved could contribute to the development of new technologies, applications, and certification procedures for the aircraft sector. Furthermore, the AI-based diagnostics’ tool could serve as a benchmark for other areas - applications such as infrastructure maintenance, marine structures monitoring, wind turbines inspection, etc.
The project will provide active collaboration and exchange of ideas and knowledge with key stakeholders within different centres of Cranfield University and industrial partners in the aircraft industry (Boeing, BAE Systems, Saab). The AI and Machine Learning based applications within IVHM centre and across other research centres would be helpful for the potential researcher in acquiring essential knowledge and building skills required for this specific research project. The IVHM Centre encourages and supports ample opportunities for disseminating individual research through reputed journals and presenting papers in high profile and well-known conferences within the UK and across the globe. More significantly, the potential candidate will have the opportunity to present his research work during quarterly technical reviews to the wider research community from within the university and the industrial partners (Boeing, Saab, BAE systems). It also provides a networking platform for promising researchers to lay the foundations of their professional relationship with key representatives from various companies.
Upon successful completion of the project, the potential candidate will be able to carry out research activities independently and more vigorously. This research will be formative for the potential candidate in building his/her skills and analytical logic for autonomous systems inspection. The understanding of the essence and application of futuristic reliable autonomous inspection system design would broaden the employability scope appreciably.
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