Real-time Construction of 3D Welding Torch in Virtual Space for Welding Training Simulator

Full Text (PDF, 1498KB), PP.34-45

Views: 0 Downloads: 0


Fangming Yuan 1,*

1. Wuhan University of Technology, Hu Bei Provence´╝îWu Han 430063,China

* Corresponding author.


Received: 16 Apr. 2019 / Revised: 3 Jul. 2019 / Accepted: 11 Aug. 2019 / Published: 8 Sep. 2019

Index Terms

Virtual and Augmented Reality Applications, Welding simulator, Real-time positioning system, Real-time visualization.


One unsolved problem in the development of an effective welding training simulator is how to construct an accurate 3D welding torch model based on the moving position of this torch in the training process. This paper presents an effective approach to deal with the problem. The whole scene is constructed in the base coordinate system and the torch is modeled as a 3D object in the sub-coordinate system. The sub-coordinate system firstly overlaps the base coordinate system, and it’s continuously changing as the trainer operates the torch. A nine axis sensor is installed in the torch at a selected point, which is the origin of the sub-coordinate system. The sensor can measure the rotation posture of the torch. Another marked point that can be captured by the Binocular Vision System (BVS) is installed with an infrared emitter. The BVS can measure the coordinate values of this point in the base coordinate system. As long as the coordinate values of a certain point on the model and its rotation posture based on this point can be determined, the VR development tool, such as Unity-3d,can track the model in real-time. That is the algorithm of this system, which is verified by Pro/E, a 3D modeling software. The approach presented above is applied to a welding training simulator product, which has been put into use and proved to be effective.

Cite This Paper

Fangming, Yuan. "Real-time Construction of 3D Welding Torch in Virtual Space for Welding Training Simulator", International Journal of Engineering and Manufacturing(IJEM), Vol.9, No.5, pp.34-45, 2019. DOI: 10.5815/ijem.2019.05.03


[1] Fast, K., Gifford, T., Yancey, R. Virtual training for welding [P]. Mixed and Augmented Reality, 2004. ISMAR 2004. Third IEEE and ACM International Symposium on, 2004.

[2] Yizhong Wang, Yonghua Chen, Wenjie Zhang, Dingcheng Liu, Huafang Huang. Study on underwater wet arc welding training with haptic device [P]. VECIMS '09. IEEE International Conference on, 2009.

[3] White, S., Prachyabrued, M., Baghi, D., Aglawe, A., Reiners, D., Borst, C., Chambers, T. Virtual Welder Trainer [P]. Virtual Reality Conference, 2009. VR 2009. IEEE, 2009.

[4] Xiwen Liu. Single neuron self-tuning PID control for welding molten pool depth [P]. Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, 2008.

[5] Xin Yin, Zhen Zhang. Defect recognized system of friction welding based on compensatory fuzzy neural network [P]. Machine Learning and Cybernetics, 2009 International Conference on, 2009.

[6] Ning Huang,YuKang Liu,Shujun Chen,YuMing Zhang. Control of human welder's arm movement in (GTAW) process [P]. Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference on, 2014.

[7] B. Xie, Q. Zhou and L. Yu, "A real-time welding training system base on virtual reality," 2015 IEEE Virtual Reality (VR), Arles, 2015, pp. 309-310. 

[8] U. Yang, G. A. Lee, Y. Kim, D. Jo, J. Choi and K. Kim, "Virtual Reality Based Welding Training Simulator with 3D Multimodal Interaction," 2010 International Conference on Cyberworlds, Singapore, 2010, pp. 150-154.

[9] S. Nawrocki, L. Hao and X. Tang, "Modeling & analysis of weld short faults of bar-wound propulsion IPM machine part II: Phase-to-phase short," 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, 2011, pp. 1-4.

[10] B. Hazel, E. Boudreault, J. Côté and S. Godin, "Robotic post-weld heat treatment for in situ repair of stainless steel turbine runners," Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry, Foz do Iguassu, 2014, pp. 1-6.

[11] H. Tokunaga, N. Matsuki, H. Sawada, T. Okano and Y. Furukawa, "A robot simulator for manufacturing tasks on a component-based software development and execution framework," (ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005., Montreal, Que., 2005, pp. 162-167.

[12] E. E. M. Mohamed, T. A. Ahmed and M. A. Sayed, "Real-time simulation of position control for linear induction motor drives using cascaded sliding mode control," 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, 2018, pp. 386-391.

[13] W. Yao and L. Ma, "Research and Application of Indoor Positioning Method Based on Fixed Infrared Beacon," 2018 37th Chinese Control Conference (CCC), Wuhan, 2018, pp. 5375-5379.

[14] G. Cao, L. Lin, H. Qiu and J. F. Pan, "Design and analysis of a dSPACE-based position control system for a linear switched reluctance motor," 2009 3rd International Conference on Power Electronics Systems and Applications (PESA), Hong Kong, 2009, pp. 1-4.

[15] T. LI and H. ZHU, "Research on model control of binocular robot vision system," 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 1794-1797.

[16] P. Hu, X. Hao, J. Li, C. Cheng and A. Wang, "Design and Implementation of Binocular Vision System with an Adjustable Baseline and High Synchronization," 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, 2018, pp. 566-570.

[17] F. Zhao and Z. Jiang, "A New Algorithm for Three-dimensional Construction Based on the Robot Binocular Stereo Vision System," 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanchang, Jiangxi, 2012, pp. 302-305.

[18] G. Yang, W. Jin and T. Xu, "Design and implementation of infrared-binocular vision system for robot navigation," 2011 Chinese Control and Decision Conference (CCDC), Mianyang, 2011, pp. 4185-4188.

[19] J. Deng, J. Li, X. Zou and F. He, "A Test System of Binocular Vision of Picking Robot," 2010 International Conference on Measuring Technology and Mechatronics Automation, Changsha City, 2010, pp. 369-372.

[20] C. Ruan, X. Gu, Y. Li, G. Zhang, W. Wang and Z. Hou, "Base frame calibration for multi-robot cooperative grinding station by binocular vision," 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), Shanghai, 2017, pp. 115-120.

[21] Wang, XW (Wang, Xuewu).Three-dimensional vision-based sensing of GTAW: a review[J].International Journal of Advanced Manufacturing Technology,2014,Vol.72,No.1-4.