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PhD in Machine Learning for Reliable Automated Inspection of Aerospace Composites (with an enhanced stipend)


   Department of Electronic and Electrical Engineering

  , Dr Kenneth Charles Burnham  Friday, September 30, 2022  Funded PhD Project (UK Students Only)

About the Project

Project Summary: This is an exciting 42-month fully funded PhD position supported by EPSRC, Spirit AeroSystems, the world’s largest composite manufacturer, and National Manufacturing Institute of Scotland (NMIS). The project has an enhanced student stipend and pays for a full Non-Destructive Evaluation (NDE) training package offered through Future Innovation in Non-destructive Evaluation Centre for Doctroral Training (FIND CDT)

Project Description: The increasing use of advanced engineering materials such as Carbon Fibre Reinforced Plastic (CFRP) composites in the aerospace industry offers enormous ecological and financial benefits as the reduced final weight of aerostructures directly helps save on fuel consumption. This trend can be seen in modern civil aircrafts such as the Airbus A380 where 25% of the weight consists of composites that are largely manufactured and supplied by Spirit AeroSystems, as the technology and market leader for years. CFRP exhibits superior mechanical tensile properties in preferred directions where the application loadings are expected to be the highest owing to the composition of carbon fibres and resin. However, a number of manufacturing defects such as pores, delamination, lack of bonding, in-plane/through-thickness fibre waviness, and changes in the fibre volume fraction can occur after molding. Since the manufactured CFRP components are safety-critical and should be of the highest integrity to be used in airframes, non-destructive testing (NDT) based on methods such as Phased Array Ultrasound Testing (PAUT) is an essential post-manufacturing stage for certification.

PAUT probes and controllers allow for individual transmit/receive of the probe array elements enabling electronic beamforming, focusing, and steering within the target material. This introduces improved inspection coverage and reliability as compared to conventional UT systems. However, manual inspection of typical large-sized aircraft components made of CFRPs such as wing covers, pressure bulkheads, fuselage, and flaps is quite a slow process creating a bottleneck in the entire production cycle. The recent advances in the deployment of industrial robots for NDT, and particularly for UT of CFRPs [1], however, have alleviated some of the hurdles for the inspection speed. Although high-speed inspection systems that sometimes reach 500 mm/s acquiring 10,000 frames/s have obvious benefits, the enormous data obtained through these should be managed and processed by intelligent algorithms to truly reach the full potential of the automated inspection.

The encoded PAUT data generated by these scans are in the form of amplitude scans (A-scans) which correspond to the amplitude versus time response of transmit-receive by each element/sub-aperture. Different projections such as B-scan, C-scan, and D-scan of the volumetric data can be produced to efficiently detect and characterize the potential bulk defects. This project will explore the implementation of automated PAUT data interpretation for CFRPs through two approaches: I) developing a low latency Deep Neural Network (DNN) to analyse the A-scan data on the fly, while the scan is being performed, for geometrical feature recognition, automated gating of time series, and defect detection, and II) developing a Multitask Network (MN) [2] for image analysis, detection of geometrical features/defects on each B-scan, D-scan, and C-scan projection, and cross-validation of findings at the combination stage. The real-time DNN applied to the A-scan data will serve to provide warnings for defects flagged during the inspection while the MN, with a potentially improved learning through different related tasks empowered by the multi-view analysis of the data, should be able to detect the defects with higher confidence.

The project is relevant to the many advanced industrial sectors such as Aerospace, Defence, Automotive & general High-Value Manufacturing striving to bring autonomy to their production/ inspection processes using machine learning.

The project will make extensive use of the £2.5 million cutting-edge Sensor Enabled Automation & Control Hub (SEARCH) hosting several advanced industrial robots and NDE equipment at the Centre for Ultrasonic Engineering (CUE) at the University of Strathclyde. The student will have access to and will work closely with the Aerospace Innovation Centre (AIC) established by Spirit AeroSystems at their Prestwick manufacturing facility and NMIS facilities in Renfrew.

The student will work within an internationally renowned and growing team of diverse and multi-disciplinary researchers and engineers, physicists, and mathematicians and will receive a full NDE training package through FIND CDT and university training for working with advanced industrial KUKA robots, different NDE controllers, and sensor technologies.

[1] Mineo, Carmelo, et al. "Flexible integration of robotics, ultrasonics and metrology for the inspection of aerospace components." AIP conference proceedings. Vol. 1806. No. 1. AIP Publishing LLC, 2017.

[2] Zhou, Yue, et al. "Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images." Medical Image Analysis 70 (2021): 101918.

Eligibility: The project is funded by EPSRC, Spirit and NMIS. Therefore, the applicant should meet the EPSRC studentship eligibility criteria:

  • Possess an Upper second (2.1) UK BEng Honours or MEng degree in relevant engineering disciplines (Electrical, Mechanical, Naval, Design and Manufacturing, etc.) or physics-related subjects
  • Be a UK or an eligible EU national and adhere to EPSRC eligibility criteria.

The knowledge and experience in Machine Learning and Deep Learning and implementation through different coding platforms such as python, MATLAB, and C are desirable.

For more information regarding the EPSRC student eligibility visit https://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/

The subjects that would be considered for the position:

EEE, Physics, Mechanical Engineering, Naval, DMEM, Mathematics

How to Apply: Candidates requiring more information, and interested in applying should email Dr. Ehsan Mohseni via .

Thereafter, they should submit their CV, academic transcript, and a covering letter outlining their suitability for the position, to .

Primary Supervisor: Dr. Ehsan Mohseni, lecturer at Centre for Ultrasonic Engineering (CUE)

Additional Supervisors:

Prof. Gareth Pierce, Spirit AeroSystems/RAE Research Chair and the Co-director of CUE

Dr. Kenneth Charles Burnham, Industrial supervisor and knowledge exchange at NMIS


Funding Notes

Funding is provided for full tuition fees (Home/EU applicants only, refer to the eligibility section). The student will receive an increased industry-supported stipend and substantial equipment and travel funds for the duration of the project.

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