This is a self-funded PhD position to work with Dr Maryam Farsi in the Centre for Digital Engineering and Manufacturing. This PhD project will focus on machine learning-based and ontology-based solutions for lifecycle cost prediction. The potential outcome of this research will tackle the challenges and limitations of both top-down and bottom-up lifecycle cost estimation approaches. The goal is to find an intelligent, effective and semi-automated solution for calculating and predicting the lifecycle cost of high-value equipment. Read more Read less

Background

How can an ontology-based intelligent system design be deployed to enhance the accuracy, effectiveness and self-adaptiveness of the future lifecycle costing for high-value equipment?

There are several challenges and limitations with the existing cost estimation approaches that make the estimates inaccurate and untrustworthy. These barriers mainly arise from several uncertainties in cost data, and the complexities with equipment and services. Too little cost data makes the cost prediction inaccurate, and too much data raises several complications with data analytics that leads to many inevitable errors. This exciting PhD focuses on advanced machine learning techniques together with system design approaches (e.g. ontology-based and agent-based modelling) to predict the cost of equipment in a more effective and automated way. This PhD research work will focus on integrated approaches and using machine learning, and experts’ elicitation methods to develop an intelligent and effective ontology system for estimating the cost of equipment throughout its life. It is also expected that the hybrid approach integrates with data mining tools to enhance the automation and self-adaptiveness of cost prediction of high-value equipment.

Aim

The aim of the PhD is to develop an intelligent, effective and self-adaptive (automated/semi-automated) model to enhance lifecycle costing for complex engineering equipment (e.g. planes, trains, ships). The potential self-adaptive model is expected to be used as a toolkit within the high-value manufacturing sectors such as aerospace, transport and marine to support engineers and maintainers with their decision makings at the early design and during the operation stages. This PhD will bring together several research themes in the fields of data mining, machine learning, artificial intelligent, cost estimation, adaptive and predictive modelling.

PhD Objectives

1. Conduct high quality research and literature review on the relevant research area
2. Identify the interactions in an ontology-based knowledge representation for lifecycle costing
3. Develop an integrated ontology-based and intelligent knowledge capture platform
4. Automate knowledge transfer in lifecycle cost estimation of high-value equipment and evaluate its impact on cost estimation accuracy


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 works on his/her research individually and collaborates with other researchers in the field at the Centre 
 

At a glance

  • Application deadlineOngoing
  • Award type(s)PhD
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberSATM0196

Entry requirements

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.

Funding

This is a self-funded PhD; open to UK, EU and International applicants.

About the sponsor

This is a self-funded PhD that includes the ability to participate in industry-led research initiatives and access to the Cranfield Doctoral Training Network.

Cranfield Doctoral Network

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 and collaborate in a multi-disciplinary environment. 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.


How to apply

To apply for this PhD opportunity please complete the application form using the button below. 

Apply now

For further information please contact:      

Name: Dr Maryam Farsi
Email: Maryam.Farsi@cranfield.ac.uk
T: (0) 1234 75 3354