Thermobot: towards semi-autonomous, thermographic detection of cracks
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Thermo-image analysisAutomatic processing of thermo-image was implemented for two kinds of sample parts: a metal crankshaft and a carbon fiber sideblade, shown in figure 3.  
Figure 3. Sample parts tested using the thermo-image analysis: a carbon fiber sideblade (left) and a metal crankshaft (right). Such sample parts were cut into smaller portions that were analyzed, in order to ease the acquisition process. The thermal excitation method is different for the two types of material, because a strong power is needed for changing the thermal state of metal, while carbon fiber would be damaged by such a strong heating. A high-power laser was used in the former case, while small portions of carbon fiber parts were heated using a thermal chuck, even though in the final system configuration this will not be feasible because of the complex geometry of such parts. Image analysis techniques employed for crack detection on metal parts focus on the laser spot. The high amount of energy concentrated in a small spot causes a strong heat flow to the surrounding areas. Cracks modify the heat transfer profile, and can therefore be detected by measuring the regularity of the heat status around the laser spot. An example of detection result can be seen in figure 4 (left): a large crack causes the heat transfer to be less effective towards the left part of the image. The analysis of heat gradients depends on multiple parameters, including the laser power and the speed at which it scans the part under inspection. Patterns are in fact circular at lower speed, around 40 mm/s, while they become oval-shaped at higher speed. Laser power also has an impact, since it should be strong enough to cause heat transfers visible in the thermo-images. Common values are around 7-10 W. Images of carbon fiber parts show a more uniform thermal status, given by the different heating system: the thermal chuck has a surface size similar to that of the sample. The parts are framed from one side, and heated on the opposite face. The alteration of the thermal status of the part is capable of highlighting intrusion of unwanted material inside the fiber element. Image processing is exploited to segment the single fiber elements and to analyze the content of each one, that must be uniform without strong gradients. In figure 4 (right) the output of the image analysis is shown: fiber elements are marked using elliptical contours, whose color indicates whether the element has uniform color (green) or not (red). Some false positives are visible towards the borders, because the contrast is too low in such locations. This will be fixed in the final system configuration, when images acquired with the final heating system will be available.  
Figure 4. Automatic detection of a crack in a metal part (left) and carbon fiber element segmentation and analysis (right).CONCLUSIONSIn this paper we presented the progress that was made towards the implementation of a system for the semi-autonomous detection of cracks in complex parts. The system is based on thermography and uses a robotic system to position the part in front of the camera. Automatic path planning allows a quick setup of the system for new geometries. First results indicated that a robust detection of the crack is possible using image analysis methods and that the automated path planning allows the inspection of a complex part in a few minutes. Future work includes the optimization of all components in order to speed up the inspection process and the setup of the whole inspection system. Additional work on defect classification and extending the range of possible materials is also needed. ACKNOWLEDGMENTSThe research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013)under grant agreement No. 284607. REFERENCES[1] K.-J. Kremer, W. Kaiser, P. Möller (1985): Das Therm-O-Matic-Verfahren – ein neuartiges Verfahren für die Inline-Prüfung von Stahlerzeugnissen auf Oberflächenfehler. Stahl u. Eisen 105, 71-76 [2] J.Baumann, U.Netzelmann, R.Bilgram, T.Hierl, V.Carl, H.R. Subach, G. Zenzinger. (2005) “Untersuchungen zu aktiver thermografischen Prüfmethoden zur Absicherung von Hochleistungsfertigungsverfahren – Ergebnisse eines BMBF-Projekts”, DGZfP-Berichtsband 94, DGZfP-Jahrestagung, Rostock [3] B. Oswald-Tranta. (2004): Thermoinductive Investigations of Magnetic Materials for Surface Cracks. Verformungskundliches Kolloquium 23, 229-237, Herausgeber: B. Buchmayr, Lehrstuhl für Umformtechnik, Montan Universität [4] Xavier P. V. Maldague. (2001). 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A Learning Approach for Adaptive Image Segmentation, in Scene Reconstruction, Pose Estimation and Tracking, Book edited by: Rustam Stolkin, ISBN 978-3-902613-06-6, pp.530, I-Tech, Vienna, Austria, June 2007 Directory: wp-content -> uploads -> 2011 2011 -> Voices of the Old Sea Norman Lewis 2011 -> Malevil Robert Merle 2011 -> The Elephant Keeper’s Children Peter Hoeg 2011 -> Jv packet •Mars Colonization Affirmative •Mars Colonization Negative 2011 -> Registration Form (Rev. 8-86) omb no. 1024-0018 2011 -> All America Looks Up to Virginia;” Virginia and the Declaration of Independence 2011 -> Rao bulletin 2011 -> Christopher Columbus was born between 25 August and 31 October 1451 in 2011 -> The Battle of Frenchtown (1855), Washington Territory: the Political and Demographic Context rev 9/4/11 2011 -> Seti aff •seti neg •Asteroids Aff Download 456.65 Kb. Share with your friends:
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