Application of pattern recognition for a welding process

• Autorzy: Haffner Oto , Kučera Erik , Kozák Štefan , Stark Erich

Identyfikatory

DOI

10.15439/2017F115

• Konferencja: Federated Conference on Computer Science and Information Systems
• Języki publikacji: EN

Abstrakty

EN

The paper deals with the development of a system for automatic weld recognition using new information technologies based on cloud computing and single-board computer in the context of Industry 4.0. The proposed system is based on a visual system for weld recognition, and a neural network based on cloud computing for real-time weld evaluation, both implemented on a single-board low-cost computer. The proposed system was successfully verified on welding samples which correspond to a real welding process in the car production process. The system considerably contributes to the welds diagnostics in industrial processes of small- and medium-sized enterprises.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 13
• Strony: 3--8
• Opis fizyczny: Bibliogr. 12 poz., fot., rys., tab.

Twórcy

autor

Haffner Oto

• Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia

autor

Kučera Erik

• Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia

autor

Kozák Štefan

• Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia

autor

Stark Erich

• Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia

Bibliografia

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• 3. Haffner, O., & Duchoň, F. (2014). Making a Map for Mobile Robot Using Laser Rangefinder. 23rd International Conference on Robotics in Alpe-Adria-Danube Region. Conference Proceedings. Bratislava: Publishing House of Slovak University of Technology.
• 4. Haffner, O., Kučera, E., & Kozák, Š. (2016). Weld Segmentation for Diagnostic and Evaluation Method. Levoča: 2016 Cybernetics & Informatics (K&I), IEEE. http://dx.doi.org/10.1109/CYBERI.2016.7438605
• 5. Hou, X., & Liu, H. (2012). Welding Image Edge Detection and Identification Research Based on Canny. 2012 International Conference on Computer Science and Service System.
• 6. Kagermann, H. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Dostupné na nternete: http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf
• 7. Liao, Z., & Sun, J. (2013). Image segmentation in weld defect detection based on modified background subtraction. 2013 6th International Congress on Image and Signal Processing (CISP). IEEE.
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• 10. Shen, Z., & Sun, J. (2013). Welding seam defect detection for canisters based on computer vision. 2013 6th International Congress on Image and Signal Processing (CISP).
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• 12. Xuming, Z., Zhouping, Y., & Youlun, Y. (2007). Edge Detection of the Low Contrast Welded Joint Image Corrupted by Noise. 8th International Conference on Electronic Measurement and Instruments. Lab., Cambridge, MA Rep. ARCRL-66-234 (II), 1994, vol. 2.