Experimental Verification of Knowledge-Based Welding Distortion Estimation Method
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-17629-6_7
Reference9 articles.
1. Shen, W., Hu, T., Zhang, C., Ye, Y., Li, Z.: A welding task data model for intelligent process planning of robotic welding. Robot. Comput. Integr. Manuf. 64(September 2019), 101934 (2020). https://doi.org/10.1016/j.rcim.2020.101934
2. Njaastad, E.B., Egeland, O.: Automatic touch-up of welding paths using 3D vision. IFAC-PapersOnLine 49(31), 73–78 (2016). https://doi.org/10.1016/j.ifacol.2016.12.164
3. Chen, C., Fang, G., Xu, Y., Lv, N., Mitchell, D.: Autonomous welding seam detecting and tracking using vision and sound sensors in robotic gas metal arc welding. In: (Chunhui) Yang, R., Takeda, Y., Zhang, C., Fang, G. (eds.) Robotics and Mechatronics. ISRM 2017. Mechanisms and Machine Science, vol. 72. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17677-8_13
4. Lund, H., Penttilä, S., Skriko, T.: A knowledge-based multipass welding distortion estimation method for a multi-robot welding off-line programming and simulation software. Procedia Manuf. 51, 302–308 (2020). https://doi.org/10.1016/j.promfg.2020.10.043
5. Zhu, J., Khurshid, M., Barsoum, Z.: Accuracy of computational welding mechanics methods for estimation of angular distortion and residual stresses. Weld World 63(5), 1391–1405 (2019). https://doi.org/10.1007/s40194-019-00746-9
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