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Intelligent Big Data Analytics for Manufacturing and Engineering Applications


Faculty of Engineering and Informatics

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Dr Savas Konur , Dr Dhaval Thakker , Prof Felician Campean , Mr E Munive Hernandez Applications accepted all year round Self-Funded PhD Students Only

About the Project

Information processing using AI and machine learning is seen as a future growth direction to deal with increasingly large and complex data-sets. The application of AI, Big Data and IoT technologies in engineering and manufacturing industry is increasingly competitive and imperative, but underutilised or ignored, especially in SMEs.

The project aims to develop new intelligent systems capable of online decision making by analysing large-scale data at real-time in highly complex engineering/manufacturing environments. The project will fully utilise Artificial Intelligence, Data Analytics, Big Data and Industry IoT technologies to offer ‘smart’ solutions for real-time monitoring of the data gathered from the production processes and extracting the information needed for making the right decisions in time to predict certain events in advance. This capability can also be used to develop fully or partially automated production systems to increase productivity and performance, and enhance optimum output rates.

The project offers the candidate new opportunities to gain invaluable experience in the relevant areas. The successful candidate will join an interdisciplinary research team, collaboration between computer science and engineering. He/she will have the opportunity to work within a dynamic, effective and multi-disciplinary team, working closely partners both from academia and industry.

Eligibility:

Candidates are expected to hold (or be about to obtain) a minimum 2:1 honours degree (or equivalent) in a related area / subject, e.g. Computer Science, Data Science, Big Data, AI, IoT, Mathematics, etc. MSc, MA or relevant experience in a related discipline is highly desirable.

Funding Notes

Applications are open all self-funded students from UK, EU and Overseas.

References

B. Chen et. al. Smart Factory of Industry 4.0: Key Technologies, Application Case and Challenges. IEEE Access, vol. 6, pp. 6505-6519, 2017.

Q. Qi, F. Tao. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0, IEEE Access, vol. 6, pp. 3585-3593, 2018.

H. Ahuett-Garza, T. Kurfess, K. Ehmann. Industry 4.0 and Smart Manufacturing. Manufacturing Letters, vol 15, Part B, 2018.

Y. Lan, S. Konur. And P. Sutcliffe. A Data Collection and Prediction Methodology for Manufacturing Process: A Case Study in Food Industry. 4th International Conference on Fuzzy Systems and Data Mining, Bangkok, Thailand, Frontiers in Artificial Intelligence and Applications, vol. 309, pp. 428-434, 2018.


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