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Ontology and rule-based reasoning for intelligent manufacturing digital twin


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  Dr Salman Saeidlou, Dr Hany Hassanin  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Canterbury Christ Church University School of Engineering, Technology and Design welcomes applications to its PhD course from self-funded applicants to study a range of PhD projects. 

Project description

In the transformed industrial systems as marked by the industry 4.0 and 5.0 era, cyber physical systems, in integration with intelligent analytics, can define a framework of big data integration to capitalize the knowledge from multiple sensor installations and historical data logs in order to make predictive intelligent decisions. For such systems researchers have defined communications protocols such as the MTConnect which can enable data acquisition of multiple sensors simultaneously. The big data utilisation requires the streams of data to be processed and analysed through predictive analytics in real-time. Given the extent of the information researchers have explored deep on the capability of algorithms to spot trends in the data and make sense of the big data in order to further optimise the manufacturing process. Such data mining techniques can be used in various processes across the supply chain from where extensive data is being collected at every instant from deployed sensors and machines, such as dirty data from machines for reliability centred maintenance, predictive manufacturing for maximal optimisation and reduction in production times, etc. As part of this system, manufacturing intelligence is a type of software that uses all available manufacturing data for analysis, prediction of future system states and the creation of graphic summaries. Manufacturing intelligence is used to support manufacturing decisions. Leveraging manufacturing intelligence enables manufacturers to query their manufacturing sites and perform activities like reporting, modelling, experimental design, system alert, recommendation, extrapolation, prediction, optimisation or simulation. The aim of this project is to develop, evaluate and demonstrate an intelligent predictive model for manufacturing purposes. The project will address the need for knowledge extraction and pattern recognition from manufacturing data which is enabled by machine learning and data mining techniques. The research will make use of data from a real workshop case study.

The predictive model will emerge from the successful completion of the following objectives:

  • Extract and identify relevant manufacturing data within the manufacturing shop floor domain
  • Cluster and build relationships among key semantics
  • Identify the quantifiable properties of the key performance indicators (KPI) and key semantics
  • Build a manufacturing ontology to align the raw SCADA data with the shop-floor key performance indicators
  • Define the data manipulation rules for calculation of properties from SCADA
  • Develop an interface between Protégé and manufacturing database in order to populate the ontology in an automatic and efficient way
  • Build the manufacturing digital twin for creating the initial data conditions for production prediction and scheduling

 Applicants must have achieved or be expected to achieve a 1st class or 2:1 honours degree or equivalent in a related discipline in Engineering, MSc in an area of engineering or have relevant industrial experience

For further information about this project and scholarship please contact: Dr Salman Saeidlou: [Email Address Removed] or Dr Hany Hassanin, [Email Address Removed]

The research of this project will be undertaken within School of Engineering, Technology, and Design (ETD) at Canterbury Christ Church University (CCCU). Canterbury Christ Church University is located in the world famous Cathedral city amongst stunning history and heritage. Canterbury is a thriving international destination, with many students and staff choosing to study and work here, making this historic, cosmopolitan city vibrant and culturally diverse. We are strongly committed to equality and recognise the value of diverse students and staff.

Postgraduate research students are part of the Canterbury Christ Church University Graduate College which is home to almost 800 research students.The Graduate College provides a wide range of researcher development training as well as academic and social activities for postgraduate students.

How to apply

Please submit your application using the online admissions portal and click on the research cluster ‘Science, Information Technology and Engineering’:


 As part of your application you will be required to submit a research proposal. Please tick the appropriate box on the proposal form to indicate that you are applying for a PhD self study project alongside a place on the PhD programme. Please specify which project you are interested in and develop your research proposal around the description of the project and research questions set out above.

For any queries about the application and admissions process please contact Postgraduate Admissions ([Email Address Removed]).

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