PhD Studentship: Multiple Sensor Data Integration and Analytics for Directed Energy Deposition Additive Manufacturing (DED-AM) Processes and Systems

Start date - 25/09/2023

Fee status of eligible applicants: UK / International

Duration of Award: 3 years.

Supervisors - 1^st Supervisor: Jialuo Ding;  2^nd Supervisor: Jian Qin

Sponsored by: WAMC departmental scholarship from WAAMMat programme, this studentship will waive the tuition fee for three years for PhD.

We are looking for a motivated candidate to pursue PhD based on a project entitled “Multiple sensor data integration analytics for directed energy deposition additive manufacturing (DED-AM) processes and systems” This opportunity arises in the field of DED-AM, and the project aims to underpin innovation in AM digitalization and intelligence based on multiple sensor integration and advanced data analytics.

Direct energy deposition additive manufacturing (DED-AM) has been employed by both academia and industry as a driving element of Industry 4.0. The benefits of DED-AM production have been proved in the last decade, in terms of customisation, complicated products, efficient supply chain, shortened leading time and environmental sustainability.

Multi-sourced data integration and analytics has been approved to exploit and discover the hidden knowledge in many additive manufacturing research fields. However, there is still a lack of data integration and analytics for DED-AM. The main challenge of such research is that the DED-AM monitoring dataset involves various formats, types, features, and dimensions which tend to be nested as multiple hierarchical structure. Without a comprehensive understanding of every dataset and process domain knowledge, it is impossible to integrate the multiple sensor data and mine the meaningful knowledge efficiently and logically.

The overall project objectives are to:

• Examine and study the multiple sensor data collected from DED-AM process and system.
• Design, develop and evaluate a multiple sensing data integration method based on the data attributes for the DED-AM process and system.

• Understanding and processing the key system and process monitoring data to discover the hidden correlations between variables and to explain the DED-AM process and system behaviours.

• Develop a knowledge-enhanced data-driven modelling method to predict process/system status and improve the relevant DED-AM process/system. 

• Method validation experiment.

Entry requirements

Applicants should have an equivalent of first or second class UK honours degree in a related discipline or subject area (e.g., electrics, mechanical, mechatronics, and manufacturing,). For international students, the English Language requirement set by Cranfield University should also be satisfied. This project would suit a candidate with genuine interest in process and system monitoring and control and additive manufacturing automation. Previous experience with system monitoring or sensor integration for other manufacturing system is also desirable. The candidate should be self-motivated, proactive, and good at communication and teamwork.

Funding

This position opens to UK, EU and international students. The cost for running experiments and accessing to research facilities will be supported by the Welding and Additive Manufacturing Centre.

How to apply

For further information please contact:
Name: Dr Jialuo Ding
Email: jialuo.ding@cranfield.ac.uk;

If you are eligible to apply for this studentship, please complete the online application form.

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