Cranfield University is currently seeking a top class candidate to undertake fundamental research in exploring how obsolescence can be optimised early on in the design phase of the life cycle of complex engineered assets, such as planes, ships, and trains.
Obsolescence risk can be mitigated by taking actions in three main areas: supply chain, design and planning. This PhD is focusing on the design angle by aiming to raise awareness of the obsolescence problem, which may lead to tackle it at the early stages by designing to minimise obsolescence impact throughout the asset lifecycle. The optimisation will trade-off different options such as the use of open system architecture, modularity, transparency, increase of standardisation in the designs and avoid using single-sourced components.
Manufacturers often decide to discontinue the manufacture of components as they launch new ones with more advanced technological features and higher commercial gains, which ultimately drastically reduces the component life cycle. The mismatch between the duration of the life cycle of the components and the systems they are in is the main cause of obsolescence issues. An obsolescence issue arises when a component is no longer; available from the stock of own spares, procurable, nor produced by its original manufacturer at the original specifications.
In sectors such as defence, aerospace, oil and gas, railway and nuclear the systems need to be supported for many decades. In these type of systems, it is not unusual that 70-80% of the electronic components become obsolete before the system has been fielded. Therefore, obsolescence has become a major issue in these sectors. For instance, the British prime contractor for the Eurofighter project declared that obsolescence is the No.2 risk to the project and it is taking vast amounts of money to design out obsolescence from one version of the aircraft to the next.
This exciting PhD is in the context of an asset manufacturer or service provider and focuses on the design and re-design scenarios in terms of how we can minimise the lifecycle impact of obsolescence in terms of cost, asset availability, and environmental impact. The project will develop a simulation platform to test different scenarios to enable design considerations. Furthermore, the project will focus on solving numerous challenges and limitations that exist in obsolescence management approaches, particularly in the design phase due to the level of uncertainty that exists. This PhD research work will focus on developing a machine learning-based method to optimise the design of the asset in terms of the obsolescence outcomes across the life cycle. This will need to be dynamic in order to cater to the scalability and adaptability of the asset over time.
The aim of the PhD is to develop a machine learning-based simulation approach to optimise the design of an asset in terms of the obsolescence outcomes. The machine learning will rely on data from past obsolescence experiences for parts, maintenance regimes, costs, and equipment usage. The project will focus only on the tangible sources of obsolescence (E.g. electronic components) and will not cover the intangibles(e.g. obsolescence of skills, people).
The PhD will rely on case studies from the aerospace, defence, and wind sectors. We are aiming to work closely with the International Institute of Obsolescence Management. The project will target to enhance the decision-making capabilities of design engineers, obsolescence managers, asset managers, and maintenance planners. This PhD will bring together a number of research themes in the fields of obsolescence management, data mining, machine learning, artificial intelligence, and dynamic modelling.
1. Develop a data structure to support the simulation needs
2. Develop a simulation platform with machine learning features to optimise the design of assets considering the obsolescence outcomes
3. Develop an uncertainty quantification approach for obsolescence that is likely to be experienced and the associated impact.
4. Apply use cases to implement the developed approach and receive feedback.
At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing which hosts cutting-edge digital engineering facilities. The student will have access to high-end computers for simulating the complex nature of maintenance. The candidate will work on his/her research individually with supervisors and collaborates with other researchers in the field at the Centre.
Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Computer Science/ Mechanical Engineering / Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.