Cutting-Edge AI Vision: Real-Time Defect Detection for High-Volume Manufacturing

Summary

Smart Manufacturing relies on perfect quality control, yet many vital checks, like visual inspection of finished products, are still done manually. In high-volume production, this human-led process is a critical bottleneck, leading to inconsistent quality, costly rework, and the risk of shipping flawed products.

This project seeks to move beyond these limitations by developing cutting-edge AI vision systems addressing an urgent industrial challenge with immediate, large-scale impact. The project will take existing knowledge in computer vision and deep learning and apply it directly to a demanding, real-world kitchen door manufacturing environment.

The core research objective is to address the current limitations of manual quality assurance at OS Doors by creating a fully automated system for visual inspection. The research will address several challenges:

1. Complex Surfaces: Developing robust algorithms (leveraging Convolutional Neural Networks and Transformers) capable of identifying tiny defects on highly complex, varied surfaces, including painted and vinyl-wrapped finishes.
2. Real-Time Performance: Optimising algorithms for unparalleled speed to ensure accurate detection in a high-volume, continuous production line.
3. Domain Adaptation: Ensuring the system can generalise to new materials and defect types with minimal retraining, a critical requirement for flexible manufacturing.

The successful PhD researcher will design, develop, and deploy the entire AI-driven quality control pipeline, creating a scalable solution that will significantly enhance product quality and manufacturing efficiency.

This is a unique opportunity for a researcher to gain invaluable experience at the intersection of academia and industry. The research will directly contribute to OS Doors' strategic automation goals, offering a rare chance to see this research work deployed and having immediate real-world effect.

The researcher will benefit from close collaboration with both the leading academic team and industrial experts, building a powerful, dual-skill profile.

Completing this project will establish you as a leading expert in industrial AI and computer vision, highly sought after in the growing Smart Manufacturing sector.

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.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

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 75%
• Research project completion within taught Masters degree or MRES
• Practice-based research experience and/or dissemination
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Publications record appropriate to career stage
• Experience of presentation of research findings
• Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

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:

• OS Doors

This scholarship will cover tuition fees and provide a maintenance allowance of £20,780 per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £1000 per annum is also available.

To be eligible for these scholarships, applicants must meet the following criteria:

• Be a UK National, or

• Have settled status, or

• Have pre-settled status, or

• Have indefinite leave to remain or enter, or

• be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

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.

Due consideration should be given to financing your studies.