Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

Application of Pattern Recognition for a Welding Process

, , ,

DOI: http://dx.doi.org/10.15439/2017F115

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 38 ()

Full text

Abstract. 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.

References

  1. Deng, S., LIpei, J., & Long, X. (2008). Detecting linear features in weld seam images based on beamlet transform. 2008 9th International Conference on Signal Processing (s. 1145-1148). IEEE.
  2. Haffner, O. (2016). Contribution to modern methods. (in slovak). Ph.D. thesis: Slovak University of Technology in Bratislava.
  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 Internete: 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.
  8. Marônek, M., & Bárta, J. (2008). Multimediálny sprievodca technológiou zvárania (elektr. monografia). Trnava: AlumniPress.
  9. Mařík, V. (2015). National initiative Industry 4.0. Available on Internet: http://download.mpo.cz/get/53723/62020/640376/priloha001.pdf
  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).
  11. Ulrich, K., Koleňák, R., & Karvanská, S. (2006). Skúšanie zvarových spojov. Bratislava: STU v Bratislave.
  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.