scholarly journals A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6928
Author(s):  
Maximilian Schmoeller ◽  
Christian Stadter ◽  
Michael Karl Kick ◽  
Christian Geiger ◽  
Michael Friedrich Zaeh

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.

Author(s):  
Maximilian Schmoeller ◽  
Christian Stadter ◽  
Michael Karl Kick ◽  
Christian Geiger ◽  
Michael Friedrich Zaeh

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing.


2018 ◽  
Author(s):  
Juan C. Caicedo ◽  
Jonathan Roth ◽  
Allen Goodman ◽  
Tim Becker ◽  
Kyle W Karhohs ◽  
...  

Identifying nuclei is often a critical first step in analyzing microscopy images of cells, and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half.


2015 ◽  
Vol 105 (11-12) ◽  
pp. 764-769
Author(s):  
R. Schmitt ◽  
G. Mallmann ◽  
P. Ackermann ◽  
J. P. Bergmann ◽  
M. Stambke ◽  
...  

Der Fachbeitrag beschreibt eine neue Methode zur Prüfung und Qualitätssicherung von hybriden Metall-Kunststoff-Verbunden – hergestellt durch laserbasiertes thermisches Fügen – unter Verwendung der optischen Kohärenztomographie (OCT). Dieser Ansatz erlaubt die Messung der Fügenaht sowie ihrer benetzten Anbindungsfläche einschließlich des Erkennens von Blasen, Fehlstellen und Schmelzeaustrieb.   This article presents a novel method for process monitoring and quality assurance of laser-based thermal joining of metal-plastic hybrids based on optical coherence tomography. This approach enables the measurement of the joint geometry as well as the detection of bubbles, imperfections, and the wetted bonding area.


2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.


Author(s):  
Bilal Hassan ◽  
Shiyin Qin ◽  
Ramsha Ahmed ◽  
Taimur Hassan ◽  
Abdel Hakeem Taguri ◽  
...  
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