About the Project
PhD Project Theme
Autonomous defect classification for ultrasonic non-destructive testing in advanced manufacturing processes
Background
Ultrasonic Testing (UT) is a popular method in advanced manufacturing to evaluate the integrity of components without having an impact of their future usefulness (non-destructive testing). Commonly, components are inspected using manual encoded solutions (conventional UT or Phased Array Ultrasonic Testing (PAUT)) and the data is then analysed by a skilled operator. As such is dependent on the operator’s testing experiences and as this could potentially lead to errors, there is an academic research and industry need to develop a system capable of Autonomous Defect Recognition (ADR), potentially to be integrated alongside existing inspection processes and systems.
Project Outline
The overall objective of this PhD is to develop a computational framework for autonomous defect classification for ultrasonic testing. Research work will focus on Machine Learning (ML) methodologies (but not limited to ML alone), with potential investigation topics on feature extraction and selection methods in spatiotemporal signals, model-based supervised and unsupervised classification algorithms, as well as autonomous and semi-autonomous model-based decision making.
The overarching aim of the computational framework is to provide interpretation of detected signals within welds or bonds and to enable these to be classified according to a predetermined set of relevant acceptance criteria. This will necessitate the development of a system that seamlessly integrates to existing phased array equipment, and following an algorithmic process, displays the outcome of the autonomous classification results in simple linguistic terms for the process operators.
The computational framework to be developed will be assessed for its generalisation properties, based on a general selection of welds and bonds but also specifically evaluated on selected material and joining techniques. Specifically, the plastic pipe welding sector has a demand for an ADR technology. Therefore the developed methods will foremost be evaluated on this type of joining. System validation will entail a library of typical flaws and will examine the integration of the proposed framework into existing inspection processes and devices. As part of this research work, the proposed system will undergo laboratory and field trials, which will provide additional feedback and opportunities for identifying research gaps and progressing this work further.
About The Department of Automatic Control and Systems Engineering, The University of Sheffield
The Department of Automatic Control and Systems Engineering (ACSE) is part of the distinguished Faculty of Engineering (>40M research income – top 3 in the UK in Engineering) at the University of Sheffield and is the only academic department in the UK devoted solely in the area of control and systems engineering. The Department has an international reputation in research excellence (REF2014: 93% of the department’s staff were considered as ‘world leading’ or ‘internationally excellent’). The Department’s Intelligent Systems and Control (ISC) group will provide the academic lead for this project.
About The Industrial Sponsor
The Lloyd’s Register Foundation funds the advancement of engineer-related education and research and supports work that enhances safety of life at sea, on land and in the air, because life matters. Lloyd’s Register Foundation is partly funded by the profits of their trading arm Lloyd’s Register Group Limited, a global engineering, technical and business services organisation.
About NSIRC
NSIRC is a state-of-the-art postgraduate engineering facility established and managed by structural integrity specialist TWI, working closely with, top UK and International Universities and a number of leading industrial partners. NSIRC aims to deliver cutting edge research and highly qualified personnel to its key industrial partners.
Candidate Requirements
Applicants must have a minimum undergraduate Honours degree (UK 2:1 or better) or MSc (Merit or Distinction) in a relevant Science or Engineering subject from a reputable institution. Overseas applicants should also submit IELTS results (with an overall score 6.5 or higher) if applicable. More details on entry requirements can be found at: https://www.sheffield.ac.uk/acse/research-degrees/applyphd
Funding Notes
This project is funded by NSIRC, TWI and The University of Sheffield. The studentship will provide successful Home/EU students with a stipend of £20k/year, for 3 years, and will cover the cost of tuition fees. Overseas applicants are welcome to apply, with total funding capped at £24k/year, for 3 years.
Funding Notes
This project is funded by Lloyds Register Foundation, TWI and academic partners. The studentship will provide successful Home/EU students with a stipend of £16k/year and will cover the cost of tuition fees. Overseas applicants are welcome to apply, with total funding capped at £24k/year
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