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Titel: Determination of Groove Filling Levels of Pressed Pipe-Fitting Connections Using Phased Array Ultrasound Evaluated by a CNN
VerfasserIn: Jacob, Kevin
Straß, Benjamin
Brosta, Nico
Presti-Senni, Jaqueline
Sprache: Englisch
Titel: Applied Sciences
Bandnummer: 16
Heft: 5
Verlag/Plattform: MDPI
Erscheinungsjahr: 2026
Freie Schlagwörter: ultrasound
phased array
convolutional neural network
pressed pipe fittings
groove filling level
NDT
NDE
DDC-Sachgruppe: 500 Naturwissenschaften
Dokumenttyp: Journalartikel / Zeitschriftenartikel
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 der Erstveröffentlichung: 10.3390/app16052273
URL der Erstveröffentlichung: https://doi.org/10.3390/app16052273
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-472429
hdl:20.500.11880/41321
ISSN: 2076-3417
Datum des Eintrags: 16-Mär-2026
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Prof. Dr.-Ing. Bernd Valeske
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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