Optimizing the Implementation of a Robotic Welding System
محورهای موضوعی : Welding , Brazing, NDT
Fionn Foley
1
(School of Science, Technology, Engineering and Mathematics, Munster Technological University, Clash, Tralee, Co. Kerry, Ireland, V92 CX88)
Chris O’Donoghue
2
(School of Science, Technology, Engineering and Mathematics, Munster Technological University, Clash, Tralee, Co. Kerry, Ireland, V92 CX88)
Joseph Walsh
3
(School of Science, Technology, Engineering and Mathematics, Munster Technological University, Clash, Tralee, Co. Kerry, Ireland, V92 CX88)
کلید واژه: Internet of Things, Fixture design, Industry 4.0, Robotic Welding, Robotic Programming, AgriTech,
چکیده مقاله :
This paper describes the research, development, and optimization of a robotic welding system at a leading Irish AgriTech company. The project required the development of robotic welding fixtures using best design principles and the generation of associated weld programs to enable the automated welding of several products in the production system of the company. This study provides complete fixtures in line with customer requirements using Autodesk Inventor CAD software. By following an integrated methodology that links the industry standard approach to fixture design with product development tools including QFD, Pugh charts, and DFMEA, a defined structure to this process is provided, while ensuring transferability throughout the industry. It was found that the generation of an industry-ready robotic welding program using Panasonic DTPS software was aided by following a prescribed methodology. This complex process was streamlined by applying the defined coordinated approach enabling the gradual knowledge growth necessary to complete an industry-ready robotic program. The study demonstrates that moving from traditional manufacturing methods to robotization is possible for an SME. The benefits for enterprises seeking to replace mechanical manufacturing processes by adopting robotic welding systems and consequently capitalizing on the potential of this robotic technology are evident.
[1] Alexandros B., Nikos, P., Babis, M., Dimitris, A. and Gregoris, M. 2018. Enabling condition-based maintenance decisions with proactive event-driven computing. Computers in industry.100:173-183.
[2] Luo, W. H. T., Ye, Y., Zhang, C. and Wei, Y. 2020. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Robotics and Computer Integrated Manufacturing. 65: 101974.
[3] Kagermann, H., Wahlster, W. and Helbig, J. 2013. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Final report of the Industrie 4.0 Working Group. Acatech, Frankfurt, Germany.
[4] Wang, B., S. Hu, J., Sun, L. and Freiheit, T. 2020. Intelligent welding system technologies: State-of-the-art review and perspectives. Journal of Manufacturing Systems. 56:373-391.
[5] Anymounuos. 2017. Weld 4.0: European welder report on existing curriculum and digitisation needs. HighSkillz, Bremen, Germany.
[6] Pan, Z., Polden, J., Larkin, N., Duin, S. V. and Norrish, J. 2012. Recent progress on programming methods for industrial robots. Robotics and Computer-Integrated Manufacturing. 28(2):87-94.
[7] Gothwal, S. and Raj, T. 2017. Different aspects in design and development of flexible fixtures: review and future directions. International Journal of Services and Operations Management. 26(3): 386-410.
[8] Boyle, I., Rong, Y. and Brown, D. C. 2011. A review and analysis of current computer-aided fixture design approaches. Robotics and Computer-Integrated Manufacturing. 27(1):1-12.
[9] Khatu, R. D., Patil, B. T., Bhise, D. K. and Vaishnav, H. B. 2021. Design of a fixture for wire-cut EDM: A generic approach. Materials Today: Proceedings. 49(5): 2034-2041.
[10] Banga, H. K., Kalra, P., Kumar, R., Singh, S. and Pruncu, C. I. 2021. Optimization of the cycle time of robotics resistance spot welding for automotive applications. The journal of Advanced Manufacturing and Processing. 3(3):e10084.
