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Mapping of Bead Geometry in Wire Arc Additive Manufacturing Systems Using Passive Vision

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Abstract

This work addresses the online extraction of the geometry characteristics (width and centerline) of deposited beads with monocular cameras for wire arc additive manufacturing (WAAM). To enable online measurement and feature extraction from captured images, an adaptive threshold is used for segmentation, a Canny algorithm for edge detection, a Hough-line transform for feature identification of the bead edges, and a filtering step to attenuate the low signal-to-noise ratios of deposition processes. Online measurements are performed in single-bead and layer (multi-bead) scenarios. The proposed vision-based solution is experimentally implemented in a WAAM robotic system composed of a welding torch, a Kuka KR90 robot arm, a power source, wire feeder, and a passive monocular camera. Experimental results illustrate the performance and effectiveness of the proposed visual-based methodology.

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Notes

  1. CMT is a modified metal inert gas welding process based on short-circuiting transfer, characterized by low heat input and no-spatter.

  2. https://www.ros.org/.

References

  • Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 8(6), 679–698.

    Article  Google Scholar 

  • Cao, Y., Zhu, S., Liang, X., & Wang, W. (2011). Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic mag welding process. Robotics and Computer-Integrated Manufacturing, 27(3), 641–645.

    Article  Google Scholar 

  • Chen, S., Qiu, T., Lin, T., Wu, L., Tian, J., Lv, W., & Zhang, Y. (2004). Intelligent technologies for robotic welding. Robotic welding, intelligence and automation (pp. 123–143). Berlin: Springer.

    Chapter  Google Scholar 

  • Chen, X. Z., Chen, S. B., & Lin, T. (2007). Recognition of macroscopic seam for complex robotic welding environment. Robotic welding, intelligence and automation (Vol. 362, pp. 171–178). Berlin: Springer.

    Chapter  Google Scholar 

  • Chu, H. H., & Wang, Z. Y. (2016). A vision-based system for post-welding quality measurement and defect detection. The International Journal of Advanced Manufacturing Technology, 86, 3007–3014.

    Article  Google Scholar 

  • Cruz, J. G., Torres, E. M., & Absi Alfaro, S. C. (2015). A methodology for modeling and control of weld bead width in the GMAW process. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 37, 1529–1541.

    Article  Google Scholar 

  • Ding, D., Pan, Z., Cuiuri, D., & Li, H. (2015). A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robotics and Computer-Integrated Manufacturing, 31, 101–110.

    Article  Google Scholar 

  • Ding, D., Pan, Z., Cuiuri, D., & Li, H. (2015). Wire-feed additive manufacturing of metal components: technologies, developments and future interests. The International Journal of Advanced Manufacturing Technology, 81, 465–481.

    Article  Google Scholar 

  • Ding, D., Pan, Z., Cuiuri, D., Li, H., van Duin, S., & Larkin, N. (2016). Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. Robotics and Computer-Integrated Manufacturing, 39, 32–42.

    Article  Google Scholar 

  • Ding, J., Colegrove, P., Mehnen, J., Ganguly, S., Sequeira Almeida, P., Wang, F., & Williams, S. (2011). Thermo-mechanical analysis of wire and arc additive layer manufacturing process on large multi-layer parts. Computational Materials Science, 50, 3315–3322.

    Article  Google Scholar 

  • Font Comas, T., Diao, C., Ding, J., Williams, S., & Zhao, Y. (2017). A passive imaging system for geometry measurement for the plasma arc welding process. IEEE Transactions on Industrial Electronics, 64(9), 7201–7209.

    Article  Google Scholar 

  • Gibson, I., Rosen, D. W., & Stucker, B. (2010). Additive manufacturing technologies. New York: Springer.

    Book  Google Scholar 

  • Pinto-Lopera, J. E., Motta, J. M. S., & Alfaro, S. C. A. (2016). Real-time measurement of width and height of weld beads in GMAW processes. Sensors (Switzerland), 16, 1–14.

    Article  Google Scholar 

  • Williams, S. W., Martina, F., Addison, A. C., Ding, J., Pardal, G., & Colegrove, P. (2016). Wire + arc additive manufacturing. Materials Science and Technology, 32, 641–647.

    Article  Google Scholar 

  • Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., & Norrish, J. (2018). A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement. Journal of Manufacturing Processes, 35, 127–139.

    Article  Google Scholar 

  • Wu, J., & Chen, S. B. (2007). Software system designs of real-time image processing of weld pool dynamic characteristics. Robotic welding, intelligence and automation (Vol. 362, pp. 303–309). Berlin: Springer.

    Chapter  Google Scholar 

  • Xiong, J., Zhang, G., Gao, H., & Wu, L. (2013). Modeling of bead section profile and overlapping beads with experimental validation for robotic GMAW-based rapid manufacturing. Robotics and Computer-Integrated Manufacturing, 29, 417–423.

    Article  Google Scholar 

  • Xiong, J., Zhang, G., Qiu, Z., & Li, Y. (2013). Vision-sensing and bead width control of a single-bead multi-layer part: Material and energy savings in GMAW-based rapid manufacturing. Journal of Cleaner Production, 41, 82–88.

    Article  Google Scholar 

  • Xu, Y., Fang, G., Chen, S., Zou, J. J., & Ye, Z. (2014). Real-time image processing for vision-based weld seam tracking in robotic GMAW. The International Journal of Advanced Manufacturing Technology, 73(9–12), 1413–1425.

    Article  Google Scholar 

  • Xu, Y., Lv, N., Fang, G., Du, S., Zhao, W., Ye, Z., & Chen, S. (2017). Welding seam tracking in robotic gas metal arc welding. Journal of Materials Processing Technology, 248, 18–30.

    Article  Google Scholar 

  • Li, Yuan, Li, You Fu, Wang, Qing Lin, De, Xu., & Tan, Min. (2010). Measurement and defect detection of the weld bead based on online vision inspection. IEEE Transactions on Instrumentation and Measurement, 59, 1841–1849.

    Article  Google Scholar 

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Acknowledgements

An early version of this paper was presented at the XXIII Congresso Brasileiro de Automática (CBA 2020). This study was financed in part by Shell Brasil Petróleo Ltda, Empresa Brasileira de Pesquisa e Inovação Industrial (Embrapii), the National Council for Scientific and Technological Development (CNPq), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES) Finance Code 001.

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Correspondence to Marcus O. Couto.

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Couto, M.O., Rodrigues, A.G., Coutinho, F. et al. Mapping of Bead Geometry in Wire Arc Additive Manufacturing Systems Using Passive Vision. J Control Autom Electr Syst 33, 1136–1147 (2022). https://doi.org/10.1007/s40313-021-00880-0

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