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  Development of a Knowledge-Based System for Smart Manufacturing Factory

   School of Computing, Engineering & the Built Environment

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  Dr Gokula Vasantha, Dr K Goh, Dr Brian Davison  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The Engineering and Mathematics Group within the School of Computing, Engineering and the Built Environment is inviting applications for a research studentship in developing a knowledge-based system for smart manufacturing factory leading to the award of a PhD degree.

Many manufacturing parameters, such as the movement of input raw materials to machine performance, impact the productivity of a manufacturing factory. Therefore, manufacturers are interested in developing data-driven tools and techniques to monitor manufacturing units in real-time and develop smart, proactive strategies to improve performances. This doctoral research project aims to develop a knowledge-based system that assesses manufacturing factory performance, develops improvement strategies, and evaluates the impact of proposed improvements. The project focuses on the following research objectives: (i) automate collecting and analysing real-time factory data, (ii) predict adaption required in a smart manufacturing factory based on real-time information, and (iii) develop a knowledge-based system for providing automatic suggestions to improve manufacturing performance.

Since knowledge discovery to improve manufacturing factory performance is the core objective, this research requires an excellent understanding of manufacturing systems, system engineering principles, data analytics, and machine learning (i.e., predictive modelling) techniques. Furthermore, the research involves a complete data processing cycle, such as multi-modal manufacturing data collection with appropriate sensors (e.g. worker’s movement, machine temperature and vibration), data integration, data cleaning and data transformation. Therefore, it would be ideal if the PhD candidate has some experience either in big data analytics or system simulation modelling software such as SimUl8 and advanced programming skills.

The research work will initiate within the Flexible Manufacturing Laboratory at Edinburgh Napier University. The development of an early Knowledge-Based System will then be studied and tested in an actual manufacturing industry. The researcher joining this project will develop and train in the appropriate technical areas. The researcher will be actively encouraged to present the work at leading international conferences and workshops. The researcher should have an appetite for undertaking an enquiring and rigorous approach to research together with a keen intellect and disciplined work habits. The researcher will benefit from collaborating with Professors at the University of Edinburgh and Strathclyde through an ongoing EPSRC (The Engineering and Physical Sciences Research Council, UK) funded research project.

Academic qualifications

A first degree (at least a 2.1) ideally in Mechanical or Data Science or Operation Research with a good fundamental knowledge of data analytics and manufacturing systems and performance analysis, or equivalent Masters degree.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes

  • Experience of fundamental intelligent manufacturing systems and processes
  • Competent in data analytics and statistical techniques
  • Knowledge of simulation processes and prediction approaches
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

For enquiries about the content of the project, please email Dr Gokula Vasantha [Email Address Removed] 

For information about how to apply, please visit our website

To apply, please select the link for the PhD Computing FT application form

Computer Science (8) Mathematics (25)

Funding Notes

The studentship covers the Home/EU level payment of full-time fees for three academic years, plus 36 monthly stipend payments at the prevailing rate set by the Research Councils.


Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. Annual Reviews in Control, 47, 200-213.
Yang, W., Fu, C., Yan, X., & Chen, Z. (2020). A knowledge-based system for quality analysis in model-based design. Journal of Intelligent Manufacturing, 31(6), 1579-1606.
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