Interpolation techniques for ultrasonic data
File(s)
Published version
OA Location
Author(s)
Sarris, Georgios
Lowe, Michael JS
Huthwaite, Peter
Type
Journal Article
Abstract
Many applications where ultrasound is used for diagnostics exist where limited data is preventing a particular approach from being fully exploited; for example, sufficient data availability would allow the qualification of non-destructive evaluation (NDE) methods in-silico, and would potentially also enable the training of machine learning algorithms related to ultrasound and its applications. Real, experimental ultrasonic data is often scarce, and while it is already known that finite element (FE) modelling produces data which is sufficiently realistic to augment real data, the computational cost associated with its generation at the scales required for the aforementioned purposes is often prohibitive. In this work, we propose the use of interpolation techniques in combination with results from FE modelling to rapidly generate more data without the need to solve additional FE models. We present the relevant methods to achieve this, and validate them through four exemplary cases of increasing complexity. Validation is achieved through the comparison of interpolation-generated results to those generated by full FE modelling, demonstrating that our method is capable of producing results for different physical setups and signals of various degrees of complexity. The results were typically within less than 1% away from the expected, but generated at a fraction of the typical computational cost, and, while the validation cases examined are of interest to the NDE community, the method extends to other fields where ultrasonic data is of interest.
Date Issued
2026-02-01
Date Acceptance
2025-09-11
Citation
Ultrasonics, 2026, 158
ISSN
0041-624X
Publisher
Elsevier
Journal / Book Title
Ultrasonics
Volume
158
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41005014
PII: S0041-624X(25)00260-4
Subjects
Acoustics
ELEMENT
FE modelling
In-silico data generation
INSPECTION
Interpolation
Life Sciences & Biomedicine
MODEL
NDE qualification
PREDICTION
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Technology
WAVES
Publication Status
Published
Coverage Spatial
Netherlands
Article Number
107823
Date Publish Online
2025-09-23