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Details of Grant 

EPSRC Reference: EP/X02427X/1
Title: Fast, efficient and reliable: digital qualification of ultrasonic inspection for safety-critical components
Principal Investigator: Huthwaite, Dr P
Other Investigators:
Lowe, Professor M Croxford, Dr AJ Wilcox, Professor PD
Researcher Co-Investigators:
Project Partners:
Airbus Operations Limited EDF Jacobs UK Limited
Kande International Ltd NDEvR Rolls-Royce Plc (UK)
Department: Mechanical Engineering
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 April 2023 Ends: 31 March 2026 Value (£): 1,008,847
EPSRC Research Topic Classifications:
Design & Testing Technology Materials testing & eng.
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Nov 2022 Digital Manufacturing Full Announced
Summary on Grant Application Form
In high-value manufacturing sectors such as aerospace and nuclear, safety is paramount. For this reason, the design and qualification of inspection for safety-critical components is a crucial part of the overall development cycle. However, current practice makes extensive use of experimental trials on physical components and mock-ups, into which artificial defects, limited to small numbers of specific test cases, must be introduced to demonstrate that they can be detected and characterised. Inspection qualification is therefore extremely time-consuming and costly (with some full mock-ups of defect-containing components costing £millions), and at odds with the general move toward agile, small-batch, bespoke, digitally-enabled manufacturing.

We propose replacing the use of these expensive, wasteful, physical test specimens with digital alternatives, to improve manufacturing efficiency. Delivering this will require high-speed, representative, realistic numerical simulation capabilities to be developed, in combination with solutions to reliably sample and interpolate across the high dimensionality of the parametric space. This virtual testing capability will enable the inspection of a high value component to be designed, optimised, and qualified before a single part has been manufactured. It will provide the basis of a simulation tool for operator training and be able to generate data at the scale and fidelity needed to train future machine learning solutions for inspection automation.

Ultrasonic array inspection will be the demonstrator case as this is the most widely used method for assessing the internal integrity of safety-critical components, both at manufacture and in service. To achieve the goal requires validated tools to synthesise authentic inspection data at scale and a methodology to robustly explore the vast parameter space of possible defects to determine inspection performance.

Our idea to achieve this ambitious vision is to approach the problem from two complimentary directions.

Bottom-up: we will make the direct numerical simulation of raw data more efficient. Building on previous world-leading research by the applicants, we will show how numerical simulation tools can be better exploited to reduce the computational burden by at least one order of magnitude.

Top-down: we will make the quantitative characterisation of the multi-dimensional parameter space to qualify inspection performance more efficient. Drawing on our domain knowledge and in extensive discussion with industrial collaborators (Rolls-Royce, EDF, Jacobs, Airbus, and KANDE), we will develop suitable surrogate modelling, sampling, and integration strategies for accurately characterising the parameter space with a small number of high-fidelity numerical simulations.

In addressing this problem we will produce a set of tools and techniques that ensure that inspection qualification is reduced in cost and complexity by orders of magnitude, leaving it fit for the future of digital manufacturing.

Key Findings
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Organisation Website: http://www.imperial.ac.uk