This project lies at the intersection of electronic hardware assurance, machine vision, and applied artificial intelligence, with a focus on non-destructive testing (NDT) techniques for complex multilayer printed circuit boards (PCBs). It draws from disciplines including electrical and electronic engineering, embedded systems, computer vision, and cybersecurity. The ability to verify hardware without damaging or disassembling it is increasingly vital as electronic systems become more compact, critical, and opaque in their construction. This is especially relevant in sectors such as defence, aerospace, automotive, and medical devices, where component authenticity, reliability, and traceability are essential. As supply chains grow more complex and globalised, trustworthy verification methods are a growing priority in both national security and commercial contexts.

Overview

To develop and evaluate a non-destructive verification method for multi-layer FR4-based printed circuit boards using advanced imaging, machine vision, and AI techniques, enabling rapid, accurate detection of deviations from original or inferred design specifications to support hardware assurance in critical electronic systems.

Why Cranfield University?

Cranfield University is a leading postgraduate institution specialising in science, engineering, and technology with strong industry and defence links. It offers facilities in non-destructive testing, imaging, and AI. This project will leverage Cranfield’s expertise in electronics assurance and defence research, enabling close collaboration with national security partners and industry. The supportive, interdisciplinary environment ensures research is practical, innovative, and aligned with real-world challenges.

Expected Impact?

This research is expected to deliver a practical, non-destructive method for verifying the internal structure of multi-layer PCBs with minimal user training and rapid turnaround—typically under one day per board. By integrating machine vision and AI for trace extraction and anomaly detection, the system will enhance confidence in the integrity of critical electronic hardware, even when original design files are unavailable. The outcomes will directly benefit assurance processes in national security and defence, and have broader applicability in sectors such as aerospace, automotive, and medical technology, where secure, reliable electronics are essential. The project will also contribute to academic knowledge in applied AI, image processing, and electronic systems verification.

Unique Selling Points

This project offers the opportunity to work on cutting-edge, real-world research with national security relevance. It combines machine vision, AI, and non-destructive testing—skills highly sought after in defence, aerospace, and advanced electronics sectors. Students will gain hands-on experience with imaging systems and PCB analysis tools, and may engage with industry or government partners. There is potential to attend technical conferences and specialist training courses in machine learning and hardware assurance. The project’s interdisciplinary nature provides a strong foundation for careers in applied research, secure electronics, or further academic study, with real impact in critical and emerging technology domains.

What will you gain from this experience?

The student will develop a strong blend of technical and transferable skills, including data analysis, AI/machine learning, image processing, and electronic hardware verification. They will gain hands-on experience with CT imaging systems, PCB design analysis, and non-destructive testing methods. The project will also build problem-solving, critical thinking, project management, and technical communication skills—essential for both industry and academia. By working in a research setting with potential industry or defence links, the student will enhance their professional network and employability in high-demand sectors such as defence, aerospace, secure electronics, and applied AI engineering.

 

At a glance

  • Application deadline10 Dec 2025
  • Award type(s)MSc by Research
  • Start date02 Feb 2026
  • Duration of award1 Year full time. 2 Years part time.
  • EligibilityUK, Rest of world
  • Reference numberCDS093

Entry requirements

Applicants should have an equivalent of first or second-class UK honours degree or equivalent in a related discipline, engineering or science (materials science/physics). The candidate should be self-motivated, have good communication skills for regular interaction with other stakeholders, with an interest for industrial research.

Funding

This opportunity is open to UK and international students. The student needs to support the MSc by Research tuition fees (£9,000/year for home (UK) student, and £ 22,175 per year for overseas students) and the living expenses (approximately £800-£1000 per month).

How to apply

For information about applications please contact: ajay.kumar@cranfield.ac.uk

Phone: +44 1234 98 8441

If you are eligible to apply for this MSc by research, please complete the

You should upload an up-to-date CV and use the Personal statement section of the form to justify your case for securing this opportunity.