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Title: Determination of Groove Filling Levels of Pressed Pipe-Fitting Connections Using Phased Array Ultrasound Evaluated by a CNN
Author(s): Jacob, Kevin
Straß, Benjamin
Brosta, Nico
Presti-Senni, Jaqueline
Language: English
Title: Applied Sciences
Volume: 16
Issue: 5
Publisher/Platform: MDPI
Year of Publication: 2026
Free key words: ultrasound
phased array
convolutional neural network
pressed pipe fittings
groove filling level
NDT
NDE
DDC notations: 500 Science
Publikation type: Journal Article
Abstract: In this paper, a method for determining the filling level of grooves (1 mm (W) × 0.25 mm (H)) in pressed titanium pipe-fitting joints is presented. The joints are inspected in a water bath using a 20 MHz phased array ultrasound, and the acquired raw B-scans are evaluated by a convolutional neural network that performs per-groove regression. Reference filling levels are obtained destructively from micrographs. Compared to X-ray computed to mography and destructive sectioning, the proposed approach overcomes the low material contrast between pipe and fitting, avoids long scan times, and enables a nondestructive, potentially inline-capable quantitative assessment of sub-millimeter grooves. A manual high-frequency ultrasound evaluation with a single probe and conceivable rule-based time-of-flight pipelines with hand-crafted echo picking and thresholds both show only moderate agreement with CT references and require substantial feature engineering for multiple echoes. In contrast, the PAUT-CNN method exploits the full raw B-scan without explicit feature design and achieves a root mean square error of about 7% of the groove filling levels on a held-out test set, corresponding to an absolute error on the order of a few tens of micrometers in groove height. This demonstrates that high-frequency phased array ultrasound combined with data-driven evaluation can quantitatively assess the filling of sub-millimeter grooves in aerospace-relevant press-fit connections.
DOI of the first publication: 10.3390/app16052273
URL of the first publication: https://doi.org/10.3390/app16052273
Link to this record: urn:nbn:de:bsz:291--ds-472429
hdl:20.500.11880/41321
ISSN: 2076-3417
Date of registration: 16-Mar-2026
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr.-Ing. Bernd Valeske
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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