Innovation Series: Advanced Science

Volume 2 · Issue 4 (2025)

Reverse Calibration Method for Welding Spots Based on PD/PS Simulation System

 

Yuan Li, Haibo Xu, Pengjie Wei, Kailin Wang, Yanbing Hu, Kang Zhu, Jun Zhou, Yang Zhang

Zhejiang Geely Run Automobile Co., Ltd. Ningbo Hangzhou Bay Branch, Hangzhou, China

 

Abstract: To address the persistent positional mismatches between simulated welding point coordinates and the actual locations of robotic equipment encountered in automotive body shop production lines, this paper introduces a novel reverse calibration methodology for welding spot trajectories. The approach is implemented within the established Process Designer/Process Simulate (PD/PS) simulation environment. Conventional methods in robot program reverse export result in significant deviations, making them inadequate to meet engineering precision requirements. This study aims to bridge this technical gap. Our solution involves the development of a dedicated RobotTool plugin. Leveraging this empirical data, a sophisticated spatial geometric transformation model is constructed. Through this model, the key transformation matrix describing the relationship between the theoretical simulation coordinate system and the actual physical coordinate system is solved. To improve calibration accuracy and enhance robustness against measurement noise, the least squares optimization algorithm is introduced in conjunction with a specifically designed error optimization function. This mechanism effectively filters high-precision inlier data points, thereby establishing a robust deviation compensation model. Comprehensive experimental validation conducted on representative production setups demonstrates the efficacy of the proposed method. It successfully achieves reverse correction of both welding points and their interconnecting trajectories, consistently controlling positional errors within a stringent tolerance of ±10mm. Crucially, for retrofit and production line upgrade projects, this method allows legacy vehicle welding programs to be directly output as production-ready, zero-debugging industrial robot code following the calibration process. This capability drastically reduces the traditionally intensive on-site commissioning workload by over 80%, thereby significantly enhancing operational efficiency and accelerating the deployment cycles for flexible manufacturing systems handling multiple vehicle models.

 

Keywords: Welding spot Trajectories; PD/PS Platform; Least Squares Optimization; Automotive Body Shop; Industrial Robot

 

References

[1]
International Federation of Robotics, World Robotics Report 2023: Industrial Robots in Automotive Manufacturing. Frankfurt: IFR Press, 2023.
[2]
Y. Zhang et al., "Digital twin-driven calibration for robotic welding systems," Robotics and Computer-Integrated Manufacturing, vol. 71, p. 102167, 2021.
[3]
G. Michalos et al., "Flexible assembly systems reconfiguration planning," CIRP Annals, vol. 69, no. 1, pp. 17–20, 2020.
[4]
P. J. Besl and N. D. McKay, "Method for registration of 3-D shapes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239–256, 1992.
[5]
V. Chebyshev et al., "Robust least squares for industrial robot calibration," Mechanism and Machine Theory, vol. 154, p. 104045, 2020.
[6]
K. Schroer et al., "Error compensation in industrial robots using neural networks," Journal of Manufacturing Systems, vol. 61, pp. 738–752, 2021.
[7]
Siemens Digital Industries Software, Tecnomatix Process Simulate 2206 Developer's Guide. Nuremberg: Siemens AG, 2023.
[8]
T. Kato et al., "Virtual commissioning framework for automotive production lines," IEEE Trans. Ind. Inform., vol. 18, no. 5, pp. 3352–3362, 2022.
[9]
B. Vogel-Heuser et al., "Digital twins in manufacturing: A systematic review," Journal of Industrial Information Integration, vol. 30, p. 100389, 2023.
[10]
M. Bader et al., "Digital twin integration in industrial robotics through secondary development frameworks," Journal of Manufacturing Systems, vol. 62, pp. 567–578, 2022.
[11]
Y. Guo et al., "Outlier rejection strategies in robotic calibration using statistical thresholding," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1245–1256, 2021.
[12]
A. Patel et al., "High-precision robotic platforms for automotive assembly applications," International Journal of Advanced Manufacturing Technology, vol. 105, no. 9, pp. 3945–3958, 2019.
[13]
J. Wang et al., "Performance evaluation metrics for robot calibration in real-world environments," Robotics and Computer-Integrated Manufacturing, vol. 68, p. 102102, 2021.
Download PDF
Innovation Series: Advanced Science, ISSN 2938-9933.