journal article·2026-02-26Open access

Determination of Groove Filling Levels of Pressed Pipe-Fitting Connections Using Phased Array Ultrasound Evaluated by a CNN

Kevin Jacob, Benjamin Straß, Nico Brosta, Jaqueline Presti-Senni

Applied Sciences· Volume 16· Issue 5

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 tomography 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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Flow Measurement and Analysis · Ultrasound Imaging and Elastography

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Chair of Cognitive Sensor Systems, Saarland University, 66123 Saarbrücken, Germany

Fraunhofer Institute for Nondestructive Testing IZFP, 66123 Saarbrücken, Germany

Funding

Bundesministerium für Wirtschaft und Energie

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