Dr Qunfen Qi
BEng, MSc, PhD
Expertise
Current positions
Senior Lecturer
School of Electrical, Electronic and Mechanical Engineering
Contact
Press and media
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Research interests
Building intelligent manufacturing systems that think, learn, and explain their decisions.
Research Vision
What if manufacturing systems could reason like human experts? I'm developing the mathematical foundations for truly intelligent manufacturing using category theory and explainable AI.
Machine 'Thinking' for Smart Manufacturing
Creating computational frameworks that enable machines to reason across multiple scales - from material properties to factory optimisation - while learning from experience and handling uncertainty.
Hierarchical Explainable AI
Developing AI systems that can explain their manufacturing decisions at any level of detail, from operator-friendly summaries to detailed technical analysis for engineers.
Why This Matters
As manufacturing becomes increasingly autonomous, we need systems that are both powerful and trustworthy. My research provides the mathematical foundations to make AI decisions transparent and reliable in critical manufacturing applications.
Research Impact
Leadership Roles:
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- UKRI EPSRC Innovation Fellow (2018-2022) - Prestigious fellowship for early-career researchers
- EPSRC Peer Review College Member - Ranked top 4% for participation (2020/21)
- International Standards Leader - Project lead for ISO/TR 24331-2 Geometrical Product Specification development
- ISO Technical Committee Expert - TC 213 (AG12, WG15, WG16), TC 184 WG26, JWG 21 (ISO/TC 184 & IEC/TC 65)
- BSI Technical Committee Member - AMT_4, TPR1/1, TPR1/8, TPR1/9
- Co-Chair, CIRP CAT 2024 - Led 18th International Conference on Computer-Aided Tolerancing
Recruiting PhD students: Passionate about explainable machine learning, mathematical AI foundations, or smart manufacturing applications
Applications:
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- Additive manufacturing optimisation
- Intelligent quality control and metrology
- Digital manufacturing workflows
- Healthcare device manufacturing
- Smart infrastructure systems
Projects and supervisions
Research projects
A mathematical foundation for dynamic information flow across multi-scale digital models of future smart manufacturing systems
Principal Investigator
Managing organisational unit
School of Electrical, Electronic and Mechanical EngineeringDates
03/07/2023 to 10/03/2024
Royal Society International Exchanges Cost Share (Italy): A mathematical foundation for dynamic information flow across multi-scale digital models of future smart manufacturing systems
Principal Investigator
Description
A digital twin is the virtual counterpart of a real manufacturing system. The role of the digital twin, for smart manufacturing, is to support methods and tools to predict and…Managing organisational unit
School of Electrical, Electronic and Mechanical EngineeringDates
11/03/2022 to 10/03/2024
EPSRC Innovation Fellowship: A semantic infrastructure for advanced manufacturing
Principal Investigator
Description
We are stepping into a new era of digitalisation. In this new era machines will communicate and exchange large amounts of data to ensure they can work harmoniously and collaboratively…Managing organisational unit
School of Electrical, Electronic and Mechanical EngineeringDates
25/06/2018 to 24/05/2022
Full-field characterisation of diamond grinding of brittle materials using industrial computed tomography
Principal Investigator
Managing organisational unit
School of Electrical, Electronic and Mechanical EngineeringDates
03/04/2017 to 02/04/2019
Publications
Recent publications
01/06/2025A multi-level root cause analysis method for production anomalies in manufacturing workshops
Journal of Manufacturing Systems
Role of Median Curves and Surfaces and Their Computations in Coordinate Metrology
Volume 2: Advanced Manufacturing
A methodology for information modelling and analysis of manufacturing processes for digital twins
Robotics and Computer-Integrated Manufacturing
Comparison of X-ray computed tomography and coordinate-measuring system dimensional measurement for additive manufacturing parts using physical and simulation methods
Measurement: Journal of the International Measurement Confederation
Enriching Laser Powder Bed Fusion Part Data Using Category Theory
Journal of Manufacturing and Materials Processing
