Automatic Welding, 2023, №10



2023 №10 (03)

DOI of Article 10.37434/as2023.10.04

2023 №10 (05)

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Avtomaticheskaya Svarka (Automatic Welding), #10, 2023, pp. 30-36

Application of neural networks for monitoring and control of the modes of flux-cored wire arc surfacing

V.G. Soloviov, Yu.M. Lankin, I.Yu. Romanova

E.O. Paton Electric Welding Institute of the NAS of Ukraine. 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: office@paton.kiev.ua

Modern level of automation of surfacing processes requires development of the appropriate computer systems for setting and maintaining the set process modes in real time, analysis, processing and, if required, automatic correction of these parameter values, allowing for their influence on penetration depth, base metal proportion in the deposited metal (BMP), spattering losses of electrode wire (SLM), as well as formation of the deposited beads, their size, and quality. Experiments were conducted and practical methods of development of neural network models (NNM) were demonstrated in the case of such parameters of fluxcored wire arc surfacing as BMP and SLM. These NNM were applied with success for prediction of the above technological parameters of arc surfacing. Widening practical application of NNM in arc surfacing requires expansion of the data base of surfacing technological parameters, material properties, quality of the obtained results, etc. The data base should be focused on application during NNM development and use. 12 Ref., 5 Tabl., 5 Fig.
Keywords: arc surfacing, surfacing modes, neural network models, surfacing mode monitoring, surfacing process control


Received: 17.07.2023

References

1. Baicun Wang, S. Jack Hu, Lei Sun et. al. (2020) Intelligent welding system technologies: State-of-the-art review and perspectives. J. of Manufacturing Systems, 56, 373-391. https://doi.org/10.1016/j.jmsy.2020.06.020
2. Zeqi, Hu, Xunpeng, Qin, Yifeng, Li et al. (2020) Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing. Journal of Mechanical Science and Technology, 34(4), 1683-1695. https://doi.org/10.1007/s12206-020-0331-0
3. Jun, Xiong, Guangjun, Zhang, Jianwen, Hu et. al. (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J. of Intelligent Manufacturing, 25(1), 157-163. https://doi.org/10.1007/s10845-012-0682-1
4. Ghanty. P., Vasudevan. M., Mukherjee, D.P. et. al. (2008) Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool. Science and Technology of Welding and Joining, 13(4), 395-401. https://doi.org/10.1179/174329308X300118
5. Sukhomay, Pal, Surjya, K. Pal, Arun, K. Samantaray (2002) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J. of Materials Processing Technology, 202(1-3), 464-474. https://doi.org/10.1016/j.jmatprotec.2007.09.039
6. Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara et. al. (2018) State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11). https://doi.org/10.1016/j.heliyon.2018.e00938
7. Ill-Soo Kim, Joon-Sik Son, Sang-Heon Lee et. al. (2004) Optimal design of neural networks for control in robotic arc welding. Robotics and Computer-Integrated Manufacturing, 20(1), 57-63. https://doi.org/10.1016/S0736-5845(03)00068-1
8. Ryabtsev, I.A., Lankin, Yu.N., Soloviov, V.G. et. al. (2015) Computer information-and-measuring system for investigation of arc surfacing processes. The Paton Welding J., 9, 32-35. https://doi.org/10.15407/tpwj2015.09.05
9. Soloviov, V.G. (2018) Internet database of arc surfacing process using flux-cored wires. The Paton Welding J., 1, 38-41. https://doi.org/10.15407/tpwj2018.01.08
10. Lankin. Yu.N., Soloviov, V.G. (2016) Information-measuring system for arc welding and surfacing. The Paton Welding J., 11, 36-42. https://doi.org/10.15407/tpwj2016.11.06
11. Beale, M.H., Hagan, M.T., Demuth, H.B. (2010) Neural network toolbox. User's guide. Version 7. USA, Natick, Mass.
12. Ryabtsev, I.A., Soloviov, V.G., Lankin, Yu.N. et. al. (2017) Computer system for automatic control of arc surfacing processes using electrode wires. The Paton Welding J., 5-6, 34-36. https://doi.org/10.15407/tpwj2017.06.07

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