A STRUCTURED LIGHT-BASED VISUAL SENSING SYSTEM FOR DETECTING MULTI-LAYER AND MULTI-TRACK WELDING

Shibo Cai, Guanjun Bao, and Jiaqing Pang

References

  1. [1] A. Rout, B.B.V.L. Deepak, and B.B. Biswal, Advances inweld seam tracking techniques for robotic welding: A review,Robotics and Computer-Integrated Manufacturing, 56, 2019,12–37.
  2. [2] X. Liu, C. Wu, G. Zhang, and C. Jia, Visual sensing of theweld pool geometry from the topside view in keyhole plasmaarc welding, Journal of Manufacturing Processes, 26, 2017,74–83.
  3. [3] X. Zhan, X. Liu, Y. Wei, W. Ou, J. Chen, and H. Liu,Numerical simulation on backward deformation of MIG multi-layer and multi-pass welding of thick invar alloy, InternationalJournal of Advanced Manufacturing Technology, 92(1–4), 2017,1001–1012.
  4. [4] M.R. Alkahari, T. Furumoto, T. Ueda, and A. Hosokawa,Melt pool and single track formation in selective laser sinter-ing/selective laser melting, Advanced Materials Research, 933,2014, 196–201.
  5. [5] T. Zhang, M. Wu, Y. Zhao, X. Chen, and S. Chen, Optimalmotion planning of mobile welding robot based on multivariablebroken line seams, International Journal of Robotics andAutomation, 29(2), 2014, 215–223.
  6. [6] W. Liu, J. Ma, G. Yang, and R. Kovacevic, Hybrid laser-arcwelding of advanced high-strength steel, Journal of MaterialsProcessing Technology, 214(12), 2014, 2823–2833.
  7. [7] W. Meng, Z. Li, J. Huang, Y. Wu, and R. Cao, Effect of gapon plasma and molten pool dynamics during laser lap weldingfor T-joints, International Journal of Advanced ManufacturingTechnology, 69(5–8), 2013, 1105–1112.
  8. [8] M. Xu, M. Zhao, and C. Zhang, Image processing method forweld quality inspection system of tailored blanks laser welding,Proc. 2010 International Conf. on Measuring Technology andMechatronics Automation, Changsha, China, 2010, 422–426.
  9. [9] T. Xu, Y. Guan, J. Liu, and X. Wu, Image-based visualservoing of helical microswimmers for planar path following,IEEE Transactions on Automation Science and Engineering,17(1), 2020, 325–333.
  10. [10] S. Chen, Y. Zhang, T. Lin, T. Qiu, and L. Wu, Welding roboticsystems with visual sensing and real-time control of dynamicweld pool during pulsed GTAW, International Journal ofRobotics and Automation, 19(1), 2004, 28–35.
  11. [11] Z. Wang, Y. Zhang, and R. Yang, Analytical reconstructionof three-dimensional weld pool surface in GTAW, Journal ofManufacturing Processes, 15(1), 2013, 34–40.
  12. [12] P. Rodriguez-Gonzalvez, M. Rodriguez-Martin, L.F. Ramos,and D. Gonzalez-Aguilera, 3D reconstruction methods andquality assessment for visual inspection of welds, Automationin Construction, 79, 2017, 49–58.
  13. [13] H.C. Nguyen and B.R. Lee, Laser-vision-based quality inspec-tion system for small-bead laser welding, International Jour-nal of Precision Engineering and Manufacturing, 15(3), 2014,415–423.
  14. [14] H. Chu and Z. Wang, A study on welding quality inspec-tion system for shell-tube heat exchanger based on machinevision, International Journal of Precision Engineering andManufacturing, 18(6), 2017, 825–834.
  15. [15] H. Chu and Z. Wang, A vision-based system for post-weldingquality measurement and defect detection, International Jour-nal of Advanced Manufacturing Technology, 86(9–12), 2016,3007–3014.
  16. [16] M. Rodriguez-Martin, S. Lagueela, D. Gonzalez-Aguilera, andP. Rodriguez-Gonzalvez, Procedure for quality inspection ofwelds based on macro-photogrammetric three-dimensional re-construction, Optics and Laser Technology, 73, 2015, 54–62.
  17. [17] B.A. Abu-Nabah, A.O. ElSoussi, and A.E.R.K. Al Alami,Virtual laser vision sensor environment assessment for surfaceprofiling applications, Measurement, 113, 2018, 148–160.
  18. [18] D. Zhang, Q. Zou, S. Guo, and H. Qu, Kinematics and perfor-mances analysis of a novel hybrid welding robot, InternationalJournal of Robotics and Automation, 35(4), 2020, 101–145.
  19. [19] G. Ye, J. Guo, Z. Sun, C. Li, and S, Zhong, Weld beadrecognition using laser vision with model-based classification,Robotics and Computer-Integrated Manufacturing, 52, 2018,9–16.
  20. [20] N.J. Orozco, P.A. Blomquist, R.B. Rudy, and S.R. Webber,Real-time control of laser-hybrid welding using weld qualityattributes, Proc. 23rd International Congress on Laser Ma-terials Processing and Laser Microfabrication, San Francisco,CA, USA, 2004, 1–10.
  21. [21] R. Dong, C. Liu, X. Wang, and X. Han, 3D path planning ofUAVs for transmission lines inspection, International Journalof Robotics and Automation, 35(4), 2020, 146–158.
  22. [22] X. Wu, D. Liu, M. Liu, C. Chen, and H. Guo, Individualized gaitpattern generation for sharing lower limb exoskeleton robot,IEEE Transactions on Automation Science and Engineering,15(4), 2018, 1459–1470.9
  23. [23] R. Usamentiaga and D.F. Garcia, Multi-camera calibration foraccurate geometric measurements in industrial environments,Measurement, 134, 2019, 345–358.
  24. [24] R. Usamentiaga, D.F. Garcia, C. Ibarra-Castanedo, and X.Maldague, Highly accurate geometric calibration for infraredcameras using inexpensive calibration targets, Measurement,112, 2017, 105–116.
  25. [25] S. Ran, L. Ye, J. Wang, and Q. Zhang, A novel camera calibra-tion method based on simulated annealing genetic algorithm,Applied Mechanics and Materials, 719–720, 2015, 1184–1190.
  26. [26] S. Remy, M. Dhome, J.M. Lavest, and N. Daucher, Hand-eye calibration, Proc. 1997 IEEE/RSJ International Conf.on Intelligent Robots and Systems, Grenoble, France, 1997,1057–1065.
  27. [27] G. Xu, Z. Hao, X. Li, J. Su, H. Liu, and X. Zhang, Calibrationmethod of laser plane equation for vision measurement adoptingobjective function of uniform horizontal height of feature points,Optical Review, 23(1), 2016, 33–39.

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