Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing
- 126 Downloads
In robotic GMAW-based additive manufacturing, the surface evenness of the deposited layer was significant to the dimensional accuracy and the stable fabrication process, and it was determined by the multi-bead overlapping distance. To obtain the optimal overlapping distance, a group of two-bead overlapping experiments was conducted with different overlapping ratio. The cross-section shape was observed and the variation of the bead profile caused by the damming up of the previous bead was investigated. The second bead profile could be fitted by a rotated varying parabola or circular arc function with the decreasing of the overlapping distance from the initial single bead width (w) to 0. A varying cross-section profile overlapping model was developed based on the actual forming characteristics of the overlapping experiment, through which the varying profile of two overlapping beads with arbitrary distance could be predicted. Then, the optimal overlapping distance was calculated under some principles to achieve a relatively flat top surface and stable overlapping process, and the multi-bead overlapping experiments were performed to validate the model. The results showed that the model could achieve an excellent approximation to the actual overlapping experiment, and the good surface evenness and stable overlapping process was obtained, which was significant to the research into the appearance optimization in GMAW-based additive manufacturing.
KeywordsAdditive manufacturing Gas metal arc welding Bead overlapping model Varying cross-section profile
The authors would like to thank all the staff of Hubei Key Laboratory of Advanced Technology for Automotive Components for supporting this work. The work was supported by the National Natural Science Foundation of China (NSFC), No. 51575415, and the National Key R&D Program of China, No. 2018YFB1106500.
- Arvo, J. (1992). Fast random rotation matrices. In Graphics gems III (IBM version) (pp. 117–120). Elsevier. https://doi.org/10.1016/b978-0-08-050755-2.50034-8.
- Bai, X., Colegrove, P., Ding, J., Zhou, X., Diao, C., Bridgeman, P., et al. (2018). Numerical analysis of heat transfer and fluid flow in multilayer deposition of PAW-based wire and arc additive manufacturing. International Journal of Heat and Mass Transfer, 124, 504–516. https://doi.org/10.1016/j.ijheatmasstransfer.2018.03.085.CrossRefGoogle Scholar
- Cho, D.-W., Park, J.-H., & Moon, H.-S. (2019). A study on molten pool behavior in the one pulse one drop GMAW process using computational fluid dynamics. International Journal of Heat and Mass Transfer, 139, 848–859. https://doi.org/10.1016/j.ijheatmasstransfer.2019.05.038.CrossRefGoogle Scholar
- Ghanty, P., Paul, S., Mukherjee, D. P., Vasudevan, M., Pal, N. R., & Bhaduri, A. K. (2007). Modelling weld bead geometry using neural networks for GTAW of austenitic stainless steel. Science and Technology of Welding and Joining, 12(7), 649–658. https://doi.org/10.1179/174329307X238399.CrossRefGoogle Scholar
- Hu, J., Guo, H., & Tsai, H. L. (2008). Weld pool dynamics and the formation of ripples in 3D gas metal arc welding. International Journal of Heat and Mass Transfer, 51(9–10), 2537–2552. https://doi.org/10.1016/j.ijheatmasstransfer.2007.07.042.CrossRefGoogle Scholar
- Hu, Z., Qin, X., Shao, T., & Liu, H. (2018). Understanding and overcoming of abnormity at start and end of the weld bead in additive manufacturing with GMAW. The International Journal of Advanced Manufacturing Technology, 95(5–8), 2357–2368. https://doi.org/10.1007/s00170-017-1392-9.CrossRefGoogle Scholar
- Jhavar, S., Jain, N. K., & Paul, C. P. (2014). Development of micro-plasma transferred arc (μ-PTA) wire deposition process for additive layer manufacturing applications. Journal of Materials Processing Technology, 214(5), 1102–1110. https://doi.org/10.1016/j.jmatprotec.2013.12.016.CrossRefGoogle Scholar
- Jiang, J., Hu, G., Li, X., Xu, X., Zheng, P., & Stringer, J. (2019a). Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network. Virtual and Physical Prototyping, 14(3), 253–266. https://doi.org/10.1080/17452759.2019.1576010.CrossRefGoogle Scholar
- Las-Casas, M. S., de Ávila, T. L. D., Bracarense, A. Q., & Lima, E. J. (2018). Weld parameter prediction using artificial neural network: FN and geometric parameter prediction of austenitic stainless steel welds. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(1), 26. https://doi.org/10.1007/s40430-017-0928-0.CrossRefGoogle Scholar
- Lei, Y., Xiong, J., & Li, R. (2018). Effect of inter layer idle time on thermal behavior for multi-layer single-pass thin-walled parts in GMAW-based additive manufacturing. The International Journal of Advanced Manufacturing Technology, 96(1–4), 1355–1365. https://doi.org/10.1007/s00170-018-1699-1.CrossRefGoogle Scholar
- Li, Y., Han, Q., Zhang, G., & Horváth, I. (2018). A layers-overlapping strategy for robotic wire and arc additive manufacturing of multi-layer multi-bead components with homogeneous layers. The International Journal of Advanced Manufacturing Technology, 96(9–12), 3331–3344. https://doi.org/10.1007/s00170-018-1786-3.CrossRefGoogle Scholar
- Prajadhiama, K. P., Manurung, Y. H., Minggu, Z., Pengadau, F. H., Graf, M., Haelsig, A., et al. (2019). Development of bead modelling for distortion analysis induced by wire arc additive manufacturing using FEM and experiment. MATEC Web of Conferences, 269, 05003. https://doi.org/10.1051/matecconf/201926905003.CrossRefGoogle Scholar
- Roberts, I. A., Wang, C. J., Esterlein, R., Stanford, M., & Mynors, D. J. (2009). A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing. International Journal of Machine Tools and Manufacture, 49(12–13), 916–923. https://doi.org/10.1016/j.ijmachtools.2009.07.004.CrossRefGoogle 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(2), 417–423. https://doi.org/10.1016/j.rcim.2012.09.011.CrossRefGoogle Scholar
- Zhou, X., Zhang, H., Wang, G., & Bai, X. (2016). Three-dimensional numerical simulation of arc and metal transport in arc welding based additive manufacturing. International Journal of Heat and Mass Transfer, 103, 521–537. https://doi.org/10.1016/j.ijheatmasstransfer.2016.06.084.CrossRefGoogle Scholar