Automatisierung in der industriellen Endoskopie/Development of new means regarding sensor positioning and measurement data evaluation – Automation of Industrial Endoscopy

2021 ◽  
Vol 111 (09) ◽  
pp. 644-649
Author(s):  
Lukas Bath ◽  
Ole Schmedemann ◽  
Thorsten Schüppstuhl
Keyword(s):  
Deep Learning ◽  
Measurement Data ◽  
Circular Path ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Complex Evaluation ◽  
Continuum Robot ◽  
Recording Conditions ◽  
Non Destructive ◽  
Probe Head

Die speziell zur Inspektion von Kavitäten eingesetzte industrielle Endoskopie ist im Gegensatz zu anderen zerstörungsfreien Prüfverfahren bisher wenig automatisiert. Dies liegt größtenteils an der anspruchsvollen Handhabung der Geräte innerhalb schwer zugänglicher Bereiche sowie der komplexen Auswertung der aufgenommen Messdaten aufgrund hochvarianter Aufnahmebedingungen. In diesem Beitrag wird ein neuartiger Kontinuumsroboter vorgestellt, der die kontaktfreie Führung des Sondenkopfs auf einer Kreisbahn erlaubt und so reproduzierbarere und bessere Sichtbedingungen schafft. Zusätzlich wird ein Konzept zur automatisierten Messdatenauswertung basierend auf Deep Learning vorgestellt.   In contrast to other non-destructive testing methods, industrial endoscopy, which is used specifically for inspecting cavities, has so far been little automated. This is largely due to the demanding handling of the devices within areas that are difficult to access as well as the complex evaluation of the recorded measurement data due to highly variant recording conditions. In this paper, a novel continuum robot is presented that enables non-contact guidance of the probe head on a circular path, creating more reproducible and improved viewing conditions. Additionally, a concept for automated measurement data analysis based on Deep Learning is presented.

scholarly journals Reliability assessment of existing bridge constructions based on results of non-destructive testing

2018 ◽  
Vol 199 ◽  
pp. 06001 ◽  
Author(s):  
Stefan Küttenbaum ◽  
Alexander Taffe ◽  
Thomas Braml ◽  
Stefan Maack
Keyword(s):  
Measurement Data ◽  
Basic Research ◽  
Relevant Information ◽  
Structural Safety ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Uncertainty In Measurement ◽  
Existing Structures ◽  
Non Destructive

The non-destructive testing methods available for civil engineering (NDT-CE) enable the measurements of quantitative parameters, which realistically describe the characteristics of existing buildings. In the past, methods for quality evaluation and concepts for validation expanded into NDT-CE to improve the objectivity of measured data. Thereby, a metrological foundation was developed to collect statistically sound and structurally relevant information about the inner construction of structures without destructive interventions. More recently, the demand for recalculations of structural safety was identified. This paper summarizes a basic research study on structural analyses of bridges in combination with NDT. The aim is to use measurement data of nondestructive testing methods as stochastic quantities in static calculations. Therefore, a methodical interface between the guide to the expression of uncertainty in measurement and probabilistic approximation procedures (e.g. FORM) has been proven to be suitable. The motivation is to relate the scientific approach of the structural analysis with real information coming from existing structures and not with those found in the literature. A case study about the probabilistic bending proof of a reinforced concrete bridge with statistically verified data from ultrasonic measurements shows that the measuring results fulfil the requirements concerning precision, trueness, objectivity and reliability.

scholarly journals Wireless power transfer-based eddy current non-destructive testing using a flexible printed coil array

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190579
Author(s):  
Lawal Umar Daura ◽  
GuiYun Tian ◽  
Qiuji Yi ◽  
Ali Sophian
Keyword(s):  
Deep Learning ◽  
Eddy Current ◽  
Feature Fusion ◽  
Non Destructive Testing ◽  
Wireless Power ◽  
Destructive Testing ◽  
Multiple Feature ◽  
Excitation Coil ◽  
Dual Resonance ◽  
Non Destructive

Eddy current testing (ECT) has been employed as a traditional non-destructive testing and evaluation (NDT&E) tool for many years. It has developed from single frequency to multiple frequencies, and eventually to pulsed and swept-frequency excitation. Recent progression of wireless power transfer (WPT) and flexible printed devices open opportunities to address challenges of defect detection and reconstruction under complex geometric situations. In this paper, a transmitter–receiver (Tx–Rx) flexible printed coil (FPC) array that uses the WPT approach featuring dual resonance responses for the first time has been proposed. The dual resonance responses can provide multiple parameters of samples, such as defect characteristics, lift-offs and material properties, while the flexible coil array allows area mapping of complex structures. To validate the proposed approach, experimental investigations of a single excitation coil with multiple receiving coils using the WPT principle were conducted on a curved pipe surface with a natural dent defect. The FPC array has one single excitation coil and 16 receiving (Rx) coils, which are used to measure the dent by using 21 C-scan points on the dedicated dent sample. The experimental data were then used for training and evaluation of dual resonance responses in terms of multiple feature extraction, selection and fusion for quantitative NDE. Four features, which include resonant magnitudes and principal components of the two resonant areas, were investigated for mapping and reconstructing the defective dent through correlation analysis for feature selection and feature fusion by deep learning. It shows that deep learning-based multiple feature fusion has outstanding performance for 3D defect reconstruction of WPT-based FPC-ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

scholarly journals Application of Deep Learning in Infrared Non-Destructive Testing

2018 ◽  
Author(s):  
B. Yousefi ◽  
D. Kalhor ◽  
R. Usamentiaga ◽  
L. Lei ◽  
C. Ibarra-Castanedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

scholarly journals A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 212
Author(s):  
Mingyu Gao ◽  
Dawei Qi ◽  
Hongbo Mu ◽  
Jianfeng Chen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Feature Extractor ◽  
Testing Technology ◽  
Excessive Consumption ◽  
Non Destructive ◽  
Deep Learning Model

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.

scholarly journals How To Develop a NDT Method For Weld Inspection in Battery Cell Manufacturing Using Deep Learning

2021 ◽  
Author(s):  
Erik Rohkohl ◽  
Mathias Kraken ◽  
Malte Schönemann ◽  
Alexander Breuer ◽  
Christoph Herrmann
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Eddy Current ◽  
Computer Tomography ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Battery Cell ◽  
Cell Manufacturing ◽  
Non Destructive ◽  
Battery Cells

Abstract Battery cells are central components of electric vehicles. It is important for automotive OEM to utilize high quality battery cells to ensure high performance and safety of their vehicles. This results in the high demand for quality control measures and inspection methods in battery cell manufacturing. Particular relevant features of battery cells are welds for the internal electrical contact. Failures of these welds are often the cause for battery defects in the field and scrap during production. Consequently, there is a strong need to evaluate all welds during manufacturing. However, there is no established method which allows a quick, comprehensive, and cheap inline measurement of the weld quality. This paper presents a new eddy current based method for non-destructive testing of seam welds as well as a machine learning approach for its validation. A deep learning model has been trained on eddy current measurements to predict results from a reference inspection method, in this case computer tomography. The results prove that eddy current measurements can be used to replicate data acquired by computer tomography which means that eddy current measurements could be a suitable candidate for non-destructive 100% inline inspection. More general, this study demonstrates how machine learning may help to get deeper insights into measurement results and to validate new non-destructive testing techniques whose detailed features are yet unknown. The presented evaluation method enables understanding the capabilities and the limits of a new technique and to extract hidden features from the data. Furthermore, the usage of machine learning allows to perform these evaluations on artificial product samples with specific defects and features, which avoids the costly production physical samples.

Non-destructive testing in civil engineering:

2019 ◽  
Author(s):  
Stefan Küttenbaum ◽  
Stefan Maack ◽  
Alexander Taffe ◽  
Thomas Braml
Keyword(s):  
Prestressed Concrete ◽  
Measurement Data ◽  
Relevant Information ◽  
Structural Safety ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Reliable Knowledge ◽  
Prestressed Concrete Bridge ◽  
Existing Bridges ◽  
Non Destructive

<p>The reassessment of bridges is becoming increasingly important. The basic requirement for analyses of structural safety is reliable knowledge about individual structures. This paper introduces the new approach to evaluate the quality of measured data gained from non-destructive testing (NDT) to provide reliable, objective, and relevant information about existing bridges. The purpose is to relate this validated knowledge to probabilistic analyses. Bridging the gap between NDT and numerical reassessments indicates reduced numerical uncertainties and residual service time extensions. This paper deals with an application of this approach using measurement data collected by ultrasonic technique at a prestressed concrete bridge.</p>

Non destructive testing of works of art by terahertz analysis

2013 ◽  
Vol 64 (2) ◽  
pp. 21001 ◽  
Author(s):  
Jean-Luc Bodnar ◽  
Jean-Jacques Metayer ◽  
Kamel Mouhoubi ◽  
Vincent Detalle
Keyword(s):  
Non Destructive Testing ◽  
Destructive Testing ◽  
Works Of Art ◽  
Non Destructive

Modeling of a sensitive element on a planar mushroom-shaped metamaterial for non-destructive testing and searching for inhomogeneities in technological media

2020 ◽  
pp. 54-59
Author(s):  
A. A. Yelizarov ◽  
A. A. Skuridin ◽  
E. A. Zakirova
Keyword(s):  
Sensitive Element ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Resonant Frequency Shift ◽  
Maltese Cross ◽  
Informative Parameters ◽  
Non Destructive ◽  
Cross Type ◽  
Electrodynamic Structure ◽  
Electrophysical Parameters

A computer model and the results of a numerical experiment for a sensitive element on a planar mushroom-shaped metamaterial with cells of the “Maltese cross” type are presented. The proposed electrodynamic structure is shown to be applicable for nondestructive testing of geometric and electrophysical parameters of technological media, as well as searching for inhomogeneities in them. Resonant frequency shift and change of the attenuation coefficient value of the structure serve as informative parameters.

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