Feature extraction of molten pool for laser welding quality real-time inspection

• Autorzy: Zhang J , Yin H , Huang H , Yang R , Yang J , Li Y

Identyfikatory

DOI

10.5277/oa150408

• Języki publikacji: EN

Abstrakty

EN

This paper proposed an image feature extraction method for laser welding molten pool inspection based on cellular neural network. TC4 titanium alloy thin plates were welded by Nd:YAG pulsed laser. A coaxial machine vision system was designed to acquire molten pool images. An auxiliary lighting source was employed to improve the molten pool image quality. By analyzing molten pool images, the welding defects such as fenestration or insufficient depth were identified. These results can be used as a feedback signal for laser power control. Experimental results showed that the proposed method can be used to improve laser welding quality.

Słowa kluczowe

EN

laser materials processing; process monitoring and control; image analysis

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2015
• Tom: Vol. 45, nr 4
• Strony: 523--533
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Zhang J

• Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Biomedical Engineering, Tianjin 300192, China
• Tianjin University of Technology and Education, Tianjin 300222, China

autor

Yin H

• Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Biomedical Engineering, Tianjin 300192, China

autor

Huang H

• Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Biomedical Engineering, Tianjin 300192, China

autor

Yang R

• Tianjin University of Technology and Education, Tianjin 300222, China

autor

Yang J

• Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Biomedical Engineering, Tianjin 300192, China

autor

Li Y

• Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Biomedical Engineering, Tianjin 300192, China

Bibliografia

• [1] HONGGANG DONG, ZHONGLIN YANG, ZENGRUI WANG, DEWEI DENG, CHUANG DONG, Vacuum brazing TC4 titanium alloy to 304 stainless steel with Cu-Ti-Ni-Zr-V amorphous alloy foil, Journal of Materials Engineering and Performance 23(10), 2014, pp. 3770–3777.
• [2] ARIF A.F.M., AL-OMARI A.S., YILBAS B.S., AL-NASSAR Y.N., Thermal stress analysis of spiral laser -welded tube, Journal of Materials Processing Technology 211(4), 2011, pp. 675–687.
• [3] SATTARI S., BISADI H., SAJED M., Mechanical properties and temperature distributions of thin friction stir welded sheets of AA5083, International Journal of Mechanics and Applications 2(1), 2012, pp. 1–6.
• [4] DEMIR A., AKMAN E., CANEL T., ERTÜRK S., ARSLAN KAYA A., Optimization of Nd:YAG laser welding of magnesium, Journal of Laser Micro/Nanoengineering 2(1), 2007, pp. 108–113.
• [5] KABIR A.S.H., CAO X., MEDRAJ M., WANJARA P., CUDDY J., BIRUR A., Effect of welding speed and defocusing distance on the quality of laser welded Ti-6Al-4V, Laser Applications in Materials Processing, October 17–21, 2010, Houston, Texas, USA, pp. 2787–2797.
• [6] WEI HUANG, KOVACEVIC R., A laser-based vision system for weld quality inspection, Sensors 11(1), 2011, pp. 506–521.
• [7] KAWAHITO Y., MIZUTANI M., KATAYAMA S., Investigation of high-power fiber laser welding phenomena of stainless steel, Transactions of JWRI 36(2), 2007, pp. 11–15.
• [8] OLSSON R., ERIKSSON I., POWELL J., LANGTRY A.V., KAPLAN A.F.H., Challenges to the interpretation of the electromagnetic feedback from laser welding, Optics and Lasers in Engineering 49(2), 2011, pp. 188–194.
• [9] TORRES-TREVIÑO L.M., REYES-VALDES F.A., LÓPEZ V., PRAGA-ALEJO R., Multi-objective optimization of a welding process by the estimation of the Pareto optimal set, Expert Systems with Applications 38(7), 2011, pp. 8045–8053.
• [10] CHUA L.O., YANG L., Cellular neural networks: theory, IEEE Transactions on Circuits and Systems 35(10), 1988, pp. 1257–1272.
• [11] CHUA L.O., YANG L., Cellular neural networks: applications, IEEE Transactions on Circuits and Systems 35(10), 1988, pp. 1273–1290.
• [12] NICOLOSI L., TETZLAFF R., ABT F., BLUG A., HOFLER H., Cellular neural network (CNN) based control algorithms for omnidirectional laser welding processes: experimental results, IEEE 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA), February 3–5, 2010, Berkeley, CA, pp. 1–6.
• [13] PONALAGUSAMY R., SENTHILKUMAR S., Investigation on Multilayer Raster Cellular Neural Network by Arithmetic and Heronian Mean RKAHeM(4,4), Proceedings of the World Congress on Engineering 2007, July 2–4, 2007, London, U.K., pp. 181–186.
• [14] GAZI O.B., BELAL M., ABDEL-GALIL H., Edge detection in satellite image using cellular neural network, International Journal of Advanced Computer Science and Applications 5(10), 2014, pp. 61–70.
• [15] BAG S., DE A., Development of efficient numerical heat transfer model coupled with genetic algorithm based optimisation for prediction of process variables in GTA spot welding, Science and Technology of Welding and Joining 14(4), 2009, pp. 333–345.