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. Since data becomes one of the most important elements in the additive manufacturing (AM) research, various process monitoring, system monitoring and data collection technologies have been adopted for different AM process systems relying on multiple sensors, in terms of electrical sensor, gas flow meter, pyrometer, process camera, oxygen sensor, etc. However, most of monitoring data collected from these sensors is focused independently and not been integrated together for deep analytics.

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. This PhD project will study multi-sourced data from DED-AM processes and systems, develop and validate a data integration and analytics methodology and application. 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.

The student will be based at the Welding and Additive Manufacturing Centre. The Centre is recognised for the impact of its research into advanced fusion-based processing / manufacturing methods on industry, through extensive MSc and PhD research, and its rolling technology development programme on large-scale additive manufacturing. This project will have close links to Sustainable Additive Manufacturing, Thermal monitoring instrumentation for metal additive manufacturing – PYRAM, and I-Break. The student will be integrated in a diverse and vibrant researcher community. In addition, opportunity for working with the Centre’s industrial partners (e.g. WAAM3D and WAAMMat) would be also provided.

The student is expected to acquire the following (including but not limited to) knowledge and skills from research in this project. 

  • Sensor integration and data acquisition, transferring and storage technologies. 
  • Advanced data analytics methods, like machine learning and Image processing techniques. 
  • Techniques, requirements, and applications of metal additive manufacturing. 
  • Reviewing literature, planning, and managing research, writing technical report, paper, presenting in meetings, conferences, and teamwork.

At a glance

  • Application deadline28 Jun 2023
  • Award type(s)PhD
  • Start date25 Sep 2023
  • Duration of award3 years
  • EligibilityUK, EU, US, Rest of World
  • Reference numberSATM363


Jialuo Ding

Jian Qin

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.


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.

About the sponsor

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

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

For further information please contact: 

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