Determination of Groove Filling Levels of Pressed Pipe-Fitting Connections Using Phased Array Ultrasound Evaluated by a CNN
Open Access
- 26 February 2026
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 16 (5) , 2273
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.Keywords
Funding Information
- Bundesministerium für Wirtschaft und Energie (20W1905D)
This publication has 17 references indexed in Scilit:
- Automated Flaw Detection in Multi-channel Phased Array Ultrasonic Data Using Machine LearningJournal of Nondestructive Evaluation, 2021
- Towards using convolutional neural network to locate, identify and size defects in phased array ultrasonic testingUltrasonics, 2021
- Augmented Ultrasonic Data for Machine LearningJournal of Nondestructive Evaluation, 2021
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsJournal of Computational Physics, 2019
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization2017
- Deep Residual Learning for Image Recognition2016
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification2015
- Deep learningNature, 2015
- Ultrasonic arrays for non-destructive evaluation: A reviewNDT & E International, 2006
- Ultrasonic Testing of Materials1990