AI-assisted structural defect detection with high resolution using acoustic data (LIUD_U26EMP)
Key Details
- Application deadline
- 31 January 2026 for International, 31 March 2026 for Home
- Location
- UEA
- Funding type
- Self-funded
- Start date
- 1 June 2026
- Mode of study
- Full-time
- Programme type
- PhD
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Project description
Primary supervisor - Dr Dianzi Liu
Reliability analysis and failure modelling is crucial and very challenging for modern electronics, especially safety critical electronics-based systems working under harsh environmental conditions. To establish a reliable failure model for solder joints which is urgently needed for prognostic and health management of electronic packaging, it is necessary to identify structural defects at the micro level scale so that the accurate life expectancy of solder joints can be predicted. However, it is challenging to inspect the defect at its early stage for examples, within 20um, due to physical limitation of non-destructive testing. Therefore, an effective approach to defect detection with a high level of resolution is a critical technique to be developed for reliable life prediction. In this project, AI empowered framework for high-resolution defect inspection of solder joints is proposed using experimental acoustic data and finite element simulations. As time constraints for cycle life prediction of soler joints in the experiments, a physics-informed neural network model will be developed to leverage the acoustic information of other solder joints as data argmentation for the joint of the interest from an experimental point of view. Meanwhile, fine finite element models for joints will be used to identify the latent relationship between the one of the interest and other joints in the same Printed Circuit Boards (PCB) so that experimental time and costs will be significantly reduced to monitor the effects of defect characteristics on the joint performance. Finally, life prediction of joints by the developed framework will be assessed by experimental tests to demonstrate its effectiveness in industrial settings.
Entry requirements
The standard minimum entry requirement is 2:1 in Mechanical Engineering or General Engineering.
Funding
This project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found here.
A bench fee is also payable in addition to the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.
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