Simulation Based Cycle Time Prediction for Robot Welding
Seongho Cho, Donguk Kim, Sang-Chul Park
- Year
- 2024
- Citations
- 1
Abstract
This paper introduces methodologies aimed at predicting the cycle time of robotic arm spot welding operations, which are essential for vehicle body assembly process plans. Predicting the cycle time of robot arm is crucial for process plan, as it is closely linked to overall production efficiency and safety considerations. However, it is common for companies in the vehicle body assembly industry to rely on rough estimates for cycle time prediction. We propose methodologies that ensure ease of use and accuracy based on simulation data to cope with this problem. This paper provides an overview of the overall process of each methodology, including data collection and model construction. Additionally, experiments are conducted to compare the performance of each methodology, with results indicating that our proposed approach outperforms conventional methods. Through this research, we found the potential for the development of advanced methods applicable in the industry.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991