{"id":352,"date":"2016-07-22T10:38:32","date_gmt":"2016-07-22T10:38:32","guid":{"rendered":"http:\/\/www.profactor.at\/en\/?page_id=352"},"modified":"2023-08-30T13:07:54","modified_gmt":"2023-08-30T12:07:54","slug":"non-destructive-inspection","status":"publish","type":"page","link":"https:\/\/www.profactor.at\/en\/research\/industrial-automation-systems\/non-destructive-inspection\/","title":{"rendered":"Non-destructive Inspection"},"content":{"rendered":"

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<\/div>
<\/div><\/div>[\/vc_column_text][vc_column_text]The ultimate goal of any kind of quality control is to avoid defective parts<\/strong>. Technologies related to achieving this goal are summarized under the strategic topic of \u201cZero Defect Manufacturing<\/strong>\u201d.<\/p>\n

PROFACTOR is developing methods that generate information in addition to results coming from quality control. These data enable the closing of the feedback loop<\/strong> from quality control to the production process, thus reducing or eliminating reject parts.<\/p>\n

The first step to generating the necessary data is a suitable sensor technology<\/strong>. For metallic<\/strong> as well as (carbon fiber) composite parts<\/strong> specific sensor systems have been developed, e.g. for surface inspection based on photometric stereo combined with physical models of the surface\u2019s reflectance properties. For detecting defects inside of parts active thermography has been developed for various kinds of materials.<\/p>\n

The interpretation of the incoming data is either done through conventional, grey-level or texture-based segmentation algorithms or \u2013 more recently \u2013 through methods based on \u201cdeep learning<\/strong>\u201d that are suitable for semantic segmentation. For the actual implementation in industrial environments the lack of training data is addressed by \u201cgenerative adversarial networks\u201d that can synthesize large quantities of image data from a smaller set of samples.<\/p>\n

Data acquired during quality control are then combined with models of the production processes. These models enable the user to keep the production within tolerance limits by proposing specific adjustments of the parameters, especially when setting up a process for new product variant. For processes with high-value parts, where complex re-work processes may exist, decision support tools<\/strong> have been developed that combine quality data with logistical part flow simulations to optimize the performance of the whole production line.<\/p>\n

The quality control systems for surface inspection are often realized in the form of inspection robots<\/strong>, especially when a full surface scan needs to be done for parts of complex shape<\/strong>. The necessary tools for coverage and motion planning, for defect detection, for backprojection of defects onto 3D CAD models have been developed for this purpose.[\/vc_column_text][vc_column_text css=”.vc_custom_1693397255628{margin-top: 100px !important;}”]<\/p>\n

Projects<\/h2>\n
\n
<\/div> Currently, the use of bio-composites is limited to less critical applications that do not have significant requirements in terms of mechanical performance. However, the use of synthetic composites made from carbon or glass fibre has several difficulties in terms recycling and in terms of dependence ...+<\/a><\/span><\/div>\n
BioStruct<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The H2020 research project DrapeBot aims at the development of a human-robot collaborative draping process for carbon fiber composite parts. The robot will drape the large, less curved areas, while the human will drape the areas of high curvature that are difficult to reach. The transfer of large pa ...+<\/a><\/span><\/div>\n
DrapeBot<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The project content of HyTechonomy is the research and development of hydrogen technologies comprising electrolyzers, storage systems and fuel cells for the energy, industry and the mobility sector in order to achieve a sustainable economy. The key topics of the project are:   Renewable Hydroge ...+<\/a><\/span><\/div>\n
Hytechonomy<\/a><\/div>\n <\/div><\/div>
\n
<\/div> A data-driven remanufacturing process for sheet metal and thermoplastic composites (COMPASS) The COMPASS project is driven by the needs to on the one hand increase the efficiency of recycling and remanufacturing processes (for sheet metal parts) and on the other hand by the need to find a solution f ...+<\/a><\/span><\/div>\n
COMPASS<\/a><\/div>\n <\/div><\/div><\/div>[\/vc_column_text][vc_column_text css=”.vc_custom_1693397266581{margin-top: 100px !important;}”]<\/p>\n

Finished Projects<\/h2>\n
\n
<\/div> For complex thermo-dynamical processes such as curing of composite parts, heat treatment, coating, the current standard approach is to use experiments supported by simulation to find a suitable \u201crecipe\u201d for the process. This recipe is then applied in series production and very often the process ...+<\/a><\/span><\/div>\n
ZDM<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The requirements for quality control, even for complex components, increase up to a 100% quality inspection.\u00a0The inspection of parts of complex shape requires robotic solutions to move a sensor system in such a way that the whole surface of the part is covered. SPIRIT aims at the development of a s ...+<\/a><\/span><\/div>\n
SPIRIT<\/a><\/div>\n <\/div><\/div>
\n
<\/div> In order to remain competitive and retain its leading manufacturing position, European industry must be able to deliver high-quality products and increase productivity while keeping costs down. The manufacturing industry is undergoing a substantial transformation due to the proliferation of new digi ...+<\/a><\/span><\/div>\n
Zero Defect Manufacturing Platform<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Major parts - including the wings of the Airbus A 380 - are made of fiber-reinforced composites. During machining - e. g. drilling - the inhomogeneity of the material in the inner walls of the boreholes can lead to fraying and loosening. However, the quality of the boreholes is essential for the str ...+<\/a><\/span><\/div>\n
H-Scan<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The SonicScan project aims at developing NDT methods based on ultrasonic testing that are suitable for primary structural parts. The main challenge is the compact shape of the parts and their high thickness. To address this problem the project builds upon the sampling phased array technology that al ...+<\/a><\/span><\/div>\n
SonicScan<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The INLINE project aims at the solution of key challenges to enable the implementation of a scalable manufacturing process for fuel cell systems. Current manufacturing processes rely on manual work that has substantial limits in terms of cycle times, costs and scalability. Developments will start wi ...+<\/a><\/span><\/div>\n
INLINE<\/a><\/div>\n <\/div><\/div>
\n
<\/div> The factors of success in the context of the digital factory are the inclusion of human within the manufacturing process as well as the consideration of their individuality and experience. Assistive systems act an important role in this field. The challenges of a nearby collaboration of human and ro ...+<\/a><\/span><\/div>\n
CompleteMe<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Die automatische Pr\u00fcfung von Composite-Bauteilen gewinnt sowohl \u00a0in der Automobilindustrie als auch in der Luftfahrt zunehmend an Bedeutung. W\u00e4hrend es bei den Produktionsverfahren substantielle Fortschritte gegeben hat, wird die Pr\u00fcfung immer noch manuell durchgef\u00fchrt, nimmt aber 30-50 Prozent ...+<\/a><\/span><\/div>\n
QualitySkill<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Initial situation: Efficient production of carbon composite parts is an important topic for aerospace, automotive and other industries. In a draping process, carbon fibre textiles are shaped in order to produce parts with complex shape. This draping process results in complex deformation of carbon f ...+<\/a><\/span><\/div>\n
FibreMap<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Holzfurnieroberfl\u00e4chen spielen als Leichtbau-Dekorteile f\u00fcr die Luftfahrtindustrie eine wichtige Rolle. Diese Oberfl\u00e4chen sind hochgl\u00e4nzend lackiert, deren Qualit\u00e4t wird im Rahmen der Abnahme von den Erstausr\u00fcstern (OEM) kritisch bewertet. Dabei werden Messger\u00e4te eingesetzt, die Kennwerte \u00fcb ...+<\/a><\/span><\/div>\n
QualityGloss<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Non-destructive testing of components is an important auxiliary process step, not only in quality control but also in regular maintenance. The detection of cracks is currently done by using magnetic particle inspection, which is a decades-old, inefficient and ecologically undesirable process. There ...+<\/a><\/span><\/div>\n
ThermoBot<\/a><\/div>\n <\/div><\/div>
\n
<\/div> Machine vision in industrial quality control, e.g. in surface inspection, generates an enormous amount of data. These data are input for machine learning structures that reproduce human decision making. The setup and optimization of such machine learning structures in industrial environments require ...+<\/a><\/span><\/div>\n
UseML<\/a><\/div>\n <\/div><\/div><\/div>[\/vc_column_text][vc_tta_accordion active_section=”1″ title=”Publications” css=”.vc_custom_1693397272261{margin-top: 100px !important;}”][vc_tta_section title=”Publications in peer reviewed Journals” tab_id=”1470573766423-49b2320a-f65c5877-fd81″][vc_column_text]Alexander Walch, Christian Eitzinger, Werner Palfinger, Sebastian Beyer, Pauline Meyr-Heye;\u00a0Reactive coverage planning for robotic NDT of complex parts;\u00a0<\/strong>accepted for: European Conference on NDT 2018<\/p>\n

Edwin Lughofer, Robert Pollak, Alexandru-Ciprian Zavoianu, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Z\u00f6rrer, Christian Eitzinger, Julia Haim, Thomas Radauer; Self-Adaptive Evolving Forecast Models with Incremental PLS Space Update for On-line Predicting Quality of Micro-fluidic Chips: <\/strong>Engineering Applications of Artificial Intelligence,Volume 68, February 2018, Pages 131–151,\u00a0https:\/\/doi.org\/10.1016\/j.engappai.2017.11.001<\/a><\/p>\n

Edwin Lughofer, Roland Richter, Ulrich Neissl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer; Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior: <\/strong>Information Sciences, Volume 420, December 2017, Pages 16-36,\u00a0https:\/\/doi.org\/10.1016\/j.ins.2017.08.012<\/a><\/p>\n

Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making<\/strong>, Information Sciences, Vol. 224, pages 62-76, DOI: 10.1016\/j.ins.2012.09.054, Mar 2013<\/p>\n

Dittrich, T. Riklin-Raviv, G. Kasprian, R. Donner, P.C.Brugger, D. Prayer, G. Langs; A Spatio-Temporal Latent Atlas for Semi-Supervised Learning of Fetal Brain Segmentations and Morphological Age Estimation<\/strong>, Accepted for publication in Medical Image Analysis, 2013<\/p>\n

Elkharraz, S. Thumfart, D. Akay, C. Eitzinger, B. Henson; Tactile texture features corresponding to human affective responses<\/strong>. Submitted to IEEE Transactions on Affective Computing<\/p>\n

Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making<\/strong>,Information Sciences, accepted, pre-press DOI: 10.1016\/j.ins.2012.09.054<\/p>\n

Gr\u00fcnauer, S. Zambal, K. B\u00fchler; \u201eDetektion von Koronararterien: Das Beste aus zwei Welten<\/strong>\u201c, Bildverarbeitung f\u00fcr die Medizin (BVM):pp. 269-273, 2011<\/p>\n

van Beilen, H. B ult, R. Renken, M. Stieger, S. T humfart, F. Cornelissen, V. Kooijman; Effects of Visual Priming on Taste-Odor Interaction<\/strong>, PLoS ONE 6(9): e23857, 2011, doi:10.1371\/journal.pone.0023857<\/p>\n

Heidl, C. Eitzinger, M. Gyimesi, F. Breitenecker; Learning over Sets with Recurrent Neural Networks: An Empirical Categorization of Aggregation<\/strong>
\nFunctions, Mathematics and Computers in Simulation 82(3), pp. 442-449, doi:10.1016\/j.matcom.2010.10.018, Nov 2011<\/p>\n

Thumfart, R. H.A.H. Jacobs, E. Lughofer, C. Eitzinger, F. W. Cornelissen, W. Groissboeck, R. Richter, \u201cModeling human aesthetic perception of visual textures \u201c, ACM Transactions on Applied Perception<\/strong>, Volume 8, Issue 5, Nov. 2011, doi:10.1145\/2043603.2043609<\/p>\n

Heidl, C. Eitzinger, M. Gyimesi, F. Breitenecker; Learning over Sets with Recurrent Neural Networks: An Empirical Categorization of Aggregation Functions<\/strong>, Mathematics and Computers in Simulation, ISSN 0378-4754, 2010<\/p>\n

Groissboeck, E. Lughofer, S. Thumfart; Associating Visual Textures with Human Perceptions using Genetic Algorithms<\/strong>, Information Sciences, vol. 180, issue 11, pp. 2065-2084, doi:10.1016\/j.ins.2010.01.035, 2010<\/p>\n

H.A.H. Jacobs, R. Renken, S. Thumfart, F. W. Cornelissen; Different Judgments about Visual Textures Invoke Different Eye Movement Patterns<\/strong>, Journal of Eye Movement Research, 3(4):2, pp. 1-13, 2010[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Peer-reviewed publications at conference proceedings” tab_id=”1476977295862-f3721a67-d5fc”][vc_column_text]Edwin Lughofer, Robert Pollak, Alexandru-Ciprian Zavoianu, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Z\u00f6rrer, Christian Eitzinger, Julia Haim, Thomas Radauer; Self-Adaptive Evolving Forecast Models with Incremental PLS Space Update for On-line Predicting Quality of Micro-fluidic Chips<\/strong>, Engineering Applications of Artificial Intelligence,Volume 68, February 2018, Pages 131–151,\u00a0https:\/\/doi.org\/10.1016\/j.engappai.2017.11.001<\/a><\/p>\n

Edwin Lughofer, Roland Richter, Ulrich Neissl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer; Explaining classifier decisions linguistically for stimulating and improving operators labeling behavior<\/strong>, Information Sciences, Volume 420, December 2017, Pages 16-36,\u00a0https:\/\/doi.org\/10.1016\/j.ins.2017.08.012<\/a><\/p>\n

Alexandru-Ciprian Zavoianu, Edwin Lughofer, Robert Pollak, Pauline Meyer-Heye, Christian Eitzinger, Thomas Radauer; Multi-Objective Knowledge-Based Strategy for Process Parameter Optimization in Micro-Fluidic Chip Production<\/strong>, 2017 IEEE Symposium Series on Computational Intelligence, accepted<\/p>\n

Tran, C. Eitzinger; ThermoBot – autonomous robotic system for thermographic detection of cracks. <\/strong>Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.391-391<\/p>\n

Eitzinger, S. Akkaladevi; Dexterous Assembler Robot Working with Embodied Intelligence<\/strong>, Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.393-393<\/p>\n

Eitzinger, K. Zhou; VALERI – Validation of Advanced, Collaborative Robotics for Industrial Applications<\/strong>. Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.392-392<\/p>\n

Eitzinger, A. Baghbanpourasl, S. Zambal; Image Processing Issues in Scanning Inspection Robots. Workshop Proceedings of IAS-13, 13th Intl.Conf.on Intelligent Autonomous Systems, Padova (Italy) July 15-19,2014, ISBN 978-88-95872-06-3, pp.394-402<\/p>\n

Traxler, P. Thanner, G. Mahler; Temporal analysis for implicit compensation of local variations of emission coefficient applied for laser induced crack checking,<\/strong> 12th International Conference on Quantitative Infrared Thermography, Bordeaux, France, 7th-11th July 2014<\/p>\n

Dittrich, T. Riklin-Raviv, G. Kasprian, R. Donner, P.C.Brugger, D. Prayer, G. Langs. A Spatio; Temporal Latent Atlas for Semi-Supervised Learning of Fetal Brain Segmentations and Morphological Age Estimation<\/strong>, Medical Image Analysis, Vol. 18(1), pp. 9-21, January 2014.<\/p>\n

Alexander Walch, Christian Eitzinger; A combined calibration of 2D and 3D sensors<\/strong>, Proceedings of the VISAPP. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Lisbon, Portugal, 5th-8th Jan. 2014<\/p>\n

Traxler, P. Thanner, P. Meyer Heyer; Design of and practical experience with a thermographic crack checking system using laser heating<\/strong>, 11th European conference on NDT, 2014 10 09 Prag, ISBN: 978-80-214-5018-9 by Brno University of Technology,\u00a0http:\/\/www.ndt.net\/events\/ECNDT2014\/app\/content\/Paper\/166_Traxler.pdf<\/a><\/p>\n

Traxler; Unterdr\u00fcckung des Emissionsgradeinflusses in der Laser angeregten Risspr\u00fcfung<\/strong>, Tagungsband der \u00d6GfTh (\u00d6sterreichische Gesellschaft f\u00fcr Thermografie), 26.9.2014 Eugendorf\/Austria<\/p>\n

Walch, C. Eitzinger. A combined calibration of 2D and 3D sensors, Proceedings of the VISAPP. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Lisbon, Portugal, 5th-8th Jan. 2014<\/p>\n

Heidl, S. Thumfart, and C. Eitzinger, Humans Differ; So Should Models. Systematic Differences Call for Per-Subject Modeling<\/strong>, ICAART 2012: Proceedings of the 4th Int. Conf. on Agents and Artificial Intelligence, pages 413-418, Vilamoura, Portugal, February 6th-8th, 2012<\/p>\n

Heidl, S. Thumfart, E. Lughofer, C. Eitzinger, E. P. Klement; Classifier-based analysis of visual inspection: Gender differences in decision-making<\/strong>, Proc. of SMC 2010, IEEE Conference on Systems, Man and Cybernetics, pp. 113-120, Istanbul, Turkey, October 2010<\/p>\n

Thumfart, J. Scharinger, C. Eitzinger; Pixel based Texture Mixing, Proc. of the 34th Workshop of the Austrian Association for Pattern Recognition<\/strong>, pp. 147-154, Zwettl, Austria, May 27-28th 2010<\/p>\n

Henson, G. Elkharraz, S. Thumfart, D. Akay, C. Eitzinger; Machine vision approach to predicting affective properties of tactile textures<\/strong>, In Proceedings of the International Conference on Kansei Engineering and Emotion Research, KEER 2010, Paris, France, March 2- 4, ISBN 978-4-9905104-0-4, pp. 2261 \u2013 2270, 2010.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Contributed talks at international conferences” tab_id=”1476977343035-97d8b0f2-1975″][vc_column_text]Thumfart, W. Palfinger, M St\u00f6ger, C. Eitzinger; Accurate Fibre Orientation Measurement for Carbon Fibre Surfaces<\/strong>, accepted for presentation at CAIP 2013, York, UK, Aug 27-29th, 2013<\/p>\n

Eitzinger, S.Ghidoni, E. Menegatti; ThermoBot: towards semi-autonomous, thermographic detection of crack<\/strong>, Proc. of the International Conference on Heating by Electromagnetic Source HES-13, pp. 461-468, Padua, May 21-24, 2013<\/p>\n

Eitzinger, PROFACTOR, Steyr-Gleink, \u00d6sterreich, G. Mahler, InfraTec, Dresden; Konzeption und Aufbau einer robotergest\u00fctzten Plattform f\u00fcr optisch angeregte W\u00e4rmefluss-Thermografie<\/strong>. Presented at DGZFP, Thermographie-Kolloquium 2013, 26. – 27. September 2013, Leinfelden-Echterdingen<\/p>\n

Traxler, PROFACTOR, Steyr-Gleink, \u00d6sterreich, S. Koch, Institut Dr. Foerster, Reutlingen; Inline-Pr\u00fcfung von warmgewalzten Stahlkn\u00fcppeln mittels W\u00e4rmeflussthermographie<\/strong>, Presented at DGZFP, Thermographie-Kolloquium 2013, 26. – 27. September 2013, Leinfelden-Echterdingen<\/p>\n

Thanner, G. Traxler, Design for Thermographic Crack Checking System using Laser Induced Heat Flux Technology<\/strong>, Presented at Factory Automation Conference 2012, Veszprem, Hungary, 21-22 May 2012 Proceedings of Factory Automation 2012, pages 122-125, Veszprem, Hungary<\/p>\n

Thumfart, W. Palfinger, C. Eitzinger; Vision based sensors enabling automated production of composite material<\/strong>.\u00a0In the Proc. of SAMPE \/ SEMAT 2012, Munich, May 24th – 25th, pp. 301 – 306, ISBN: 978-3-952 3565-6-2, 2012[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Book Chapters” tab_id=”1476977402152-0eb6100f-2190″][vc_column_text]Eitzinger, S. Thumfart: Optimizing Feature Calculation in Adaptive Machine Vision Systems<\/strong>, M. Sayed-Mouchaweh and E. Lughofer (eds.), Learning in Non-Stationary Environments: Methods and Applications, DOI 10.1007\/978-1-4419-8020-5_13, Springer Science+Business Media New York 2012[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Dissertation” tab_id=”1476977502846-9946c53e-c3cb”][vc_column_text]S.Thumfart, PhD Thesis: Genetic Texture Synthesis<\/strong>. Johannes Kepler University Linz, Department of Computational Perception, Feb 2012[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Other talks and publications” tab_id=”1476977548966-f1a16b37-661c”][vc_column_text]Dittrich; Ein Atlas der fr\u00fchen Gehirnentwicklung<\/strong>. Published online at ORF Science, July 2013<\/p>\n

Thanner: \u201cDefect Avoidance, Machine-vision system catches defects in seamless steel tube production using linescan cameras and nearinfrared imaging<\/strong>“, Vision Systems Design (VSD) Magazin, 1.6. 2010<\/p>\n

W\u00f6gerer, P. Thanner, G. Traxler: \u201cMeasurement of Material properties with Thermography<\/strong>“, FACTORY AUTOMATION 2011 Conference, Gy\u00f6r, Hungary, 24-26 May 2011<\/p>\n

W\u00f6gerer, P. Thanner, G. Traxler: \u201eThermografic methods for online control for steel pipes<\/strong>“, FACTORY AUTOMATION 2011 Conference, Gy\u00f6r, Hungary, 24-26 May 2011<\/p>\n

Petra Thanner \u201eM\u00fclltrennung mit Infrarottechnologie<\/strong>\u201c, Newsletter E!AT aktuell, M\u00e4rz 2010<\/p>\n

Thumfart; \u201cPixel based Texture Mixing<\/strong>“, \u00d6AGM 2010 \u2013 34th annual workshop of the Austrian Association for Pattern Recognition (AAPR) \u2013 Computer Vision in a Global Society, Zwettl, 28. Mai 2010<\/p>\n

Thanner, W. Palfinger, \u201cQualit\u00e4tssicherung von Carbonfaserteilen mit Bildverarbeitung<\/strong>”
\nHandhabungstechnik \u2013 Der Schl\u00fcssel f\u00fcr eine automatisierte Herstellung von Composite-Bauteilen, Augsburg, 8. Juli 2010<\/p>\n

Thanner, W. Palfinger, G. Traxler, \u201cW\u00e4rmeflussauswertung f\u00fcr die induktiv angeregte Risspr\u00fcfung<\/strong>“, Thermografieforum Eugendorf, Eugendorf, 10. September 2010<\/p>\n

Eitzinger; \u201cAdaptive Produktion<\/strong>“, 25 Jahre Eureka, Linz, 7. Oktober 2010<\/p>\n

Thanner; \u201cEM80 \u2013 OIDIPUS, Optimized InGaAS Detectors for Imaging Applications and Industrial Spectroscopy<\/strong>“, 25 Jahre Eureka, Linz, 7. Oktober 2010<\/p>\n

Heidl; “Classifier \u2013 based analysis of visual inspection: Gender differences in decision-making<\/strong>“, SMC2010, IEEE International Conference on Systems, Man and Cybernetics, Istanbul, 11. Oktober 2010<\/p>\n

Thanner, G. Traxler; \u201cAdvanced Evaluation for Thermographic Crack Detection with Inductive Excitation for Steele Billets<\/strong>“, 20th Manufuturing Confernece, Budapest, 20. Oktober 2010<\/p>\n

Traxler; \u201eAutomatisierte Inline-Pr\u00fcfm\u00f6glichkeit mit aktiver Thermographie<\/strong>\u201c, Seminar W\u00e4rmefluss-Thermographie, Erlangen, 4. November 2010<\/p>\n

Petra Thanner; Defect Avoidance, Machine-vision system catches defects in seamless steel tube production using linescan cameras and near-infrared imaging,<\/strong>Vision Systems Design (VSD) Magazi, 1.6. 2010<\/p>\n

Thanner, G. Traxler; Qualit\u00e4tssicherung von Carbonfaserteilen mittels Bildverarbeitung<\/strong>, 8. Juli 2010, Handhabungstechnik – Der Schl\u00fcssel f\u00fcr eine automatisierte Herstellung von Composite-Bauteilen, Augsburg, Germany[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][vc_column width=”1\/4″ el_class=”page-sidebar page-reference-sidebar”][vc_column_text el_class=”reference-person”]<\/p>\n

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Your Contact<\/h4>\n

Dr. Christian Eitzinger<\/strong>
\nHead of Machine Vision<\/p>\n

+43 7252 885 250
\nchristian.eitzinger@null<\/span>profactor.at<\/p>\n<\/div>\n

[\/vc_column_text][vc_column_text el_class=”cta-area”]<\/p>\n

We answer…<\/h3>\n

… your Questions<\/a>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n