Real-time Weld Monitoring System for Robotic Arc Welding Process

Apply and key information  

This project is funded by:

    • Department for the Economy (DfE)

Summary

Robotic welding is used for joining large, high-value metal components. Yet, when quality drifts go unnoticed with bead misalignment, arc instability, or defects, the manufacturers face rework, waste, and delays.

This project tackles that challenge by developing camera-based process monitoring system that observes the weld in real time, detects and predicts anomalies as they emerge, and provides clear, actionable feedback.

The goal is simple and impactful: keep quality on target from the first millimetre to the last, especially on large structures where deviations are costly. Insights from monitoring system will support control of welding parameters and help optimize robot tool paths.

The aim is to achieve a right-first-time approach, with additional benefits of reducing energy and material usage.

The work will be undertaken in close collaboration with Nugent Engineering Ltd, ensuring the research is driven by real production needs and validated in industrial setting.  

The candidate will gain hands-on experience with modern robotic welding cell, advanced imaging sensors (including high dynamic range camera and arc/melt-pool vision), real-time data acquisition, and in-house metallurgical evaluation.

Along the way, candidate will build practical skills in robot programming, defect detection, sensor integration, and process optimisation.

The successful candidate will also help translate monitoring outputs into simple shop-floor indicators and operator alerts, bridging the gap between data and decision.

The candidate will be well prepared for careers in smart manufacturing, robotics, and digital quality, equipped to help industry deliver cleaner, faster, and more consistent production.

The candidate should have, or be willing to develop, programming skills for defect detection and optimise robotic tool paths. As part of the project, you will also carry out materials characterisation and mechanical testing to validate weld quality.

The successful candidate will have the opportunity to work closely with industry partner, gaining valuable industrial experience on real-world robotic welding applications.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 65%
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

  • Department for the Economy (DfE)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £21,000 (approximately) per annum for three years* (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

*Part time PhD scholarships may be available to home candidates, based on 0.5 of the full time rate, and will require a six year registration period.

Due consideration should be given to financing your studies.

Recommended reading

1. Bacioiu D, Papaelias M, Shaw R (2019) Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. J. Manuf. Process. 45:603–613. https://doi.org/10.1016/j.jmapro.2019.07.020  

2. Wang R, Wang H, He Z, et al (2024) WeldNet: a lightweight deep learning model for welding defect recognition. Weld. World. https://doi.org/10.1007/s40194-024-01759-9

3. J. Franke, F. Heinrich, R.T. Reisch, “Vision based process monitoring in wire arc additive manufacturing (WAAM)”. J Intell Manuf (2024). https://doi.org/10.1007/s10845-023-02287-x

4. Xiris XVC-1000e Welding Monitoring Camera, [Online]. Available: https://www.xiris.com/xiris-xvc-1000/

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 27 February 2026
04:00PM

Interview Date
March 2026

Preferred student start date
14th September 2026

Applying

Apply Online  

Contact supervisor

Dr Sagar Nikam

Other supervisors