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Osman A.,Fraunhofer Development Center X ray Technologies | Kaftandjian V.,INSA Lyon | Hassler U.,Fraunhofer Development Center X ray Technologies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

In this contribution we present a classification method based on the evidence theory where a comparison between modeling with and without conflict is presented as well as a comparison between the orthogonal and cautious fusion rules. The classification rules are compared to the state of the art support vector machine classifier on an industrial ultrasonic dataset. Keywords: Evidence theory, non-destructive testing, defects classification. © Springer International Publishing Switzerland 2014.


Osman A.,Fraunhofer Development Center X ray Technologies | Kaftandjian V.,INSA Lyon | Hassler U.,Fraunhofer Development Center X ray Technologies
Journal of Electronic Imaging | Year: 2012

The three dimensional (3D) X-ray computed tomography (3D-CT) has proven its successful application as an inspection method in nondestructive testing. The generated 3D volume uses high efficiency reconstruction algorithms containing all required information on the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions that need to be classified as defective or false alarms. This paper deals with the classification step using data fusion theory, which was successfully applied on 2D X-ray data in previous work along with a support vector machine (SVM). For this study we chose a 3D-CT dataset of aluminium castings that needs to be fully inspected via X-ray CT to ensure their quality. We achieved a true classification rate of 97% on a validation dataset, which proves the effectiveness of the data fusion theory as a method to build a better classifier. Comparison with SVMs shows the importance of selecting the most pertinent features to improve the classifier performance and attaining 98% of true classification rate. © 2012 SPIE and IS&T.


Osman A.,Fraunhofer Institute for Non-Destructive Testing | Hassler U.,Fraunhofer Development Center X ray Technologies | Kaftandjian V.,INSA Lyon | Hornegger J.,Friedrich - Alexander - University, Erlangen - Nuremberg
Insight: Non-Destructive Testing and Condition Monitoring | Year: 2015

Manual ultrasonic testing is frequently applied to inspect the quality of carbon fibre-reinforced polymer composites (CFRP), especially in the aerospace industry. There is an immediate requirement to replace manual ultrasonic testing and data evaluation with an automated system. A part of the solution is provided via automated acquisition of ultrasound data. The second part to be solved is the analysis of the enormous amount of data generated. The objective of this contribution is to propose a method that can provide an automated interpretation of data. The proposed method is tested on two CFRP samples with artificial defects of different sizes, depths and shapes. © 2015 Official Journal of The British Institute of Non-Destructive Testing.


Mueller A.,Fraunhofer Development Center X ray Technologies | Hassler U.,Fraunhofer Development Center X ray Technologies | Zaeh J.,Nurnberg University of Applied Sciences
RILEM Bookseries | Year: 2012

The aim of the presented work was the characterization of synthetically manufactured emeralds by means of X-ray micro computed tomography (CT). As the result of the CT measurements, a three-dimensional volume representation of the gemstones became available. Three synthetic emeralds were measured containing different kinds of inclusions. For data evaluation, an image processing chain was designed, which is adapted to the particular requirements of emerald analysis. An essential aspect was the extraction of inclusions and inhomogeneities from the reconstructed volumetric data and their characterization regarding morphology. Furthermore, results of microscopic investigation of the samples have been compared to results of CT measurements demonstrating the suitability of CT for fi nding inclusions in emeralds. © RILEM 2013.

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