Simulation Based Cycle Time Prediction for Robot Welding

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.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/wsc63780.2024.10838830
Primary Topic
Manufacturing Process and Optimization
Type
article
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article

Simulation Based Cycle Time Prediction for Robot Welding

Donguk Kim, Seongho Cho, Sang-Chul Park
Manufacturing Process and Optimization
article

Simulation Based Cycle Time Prediction for Robot Welding

Donguk Kim, Seongho Cho, Sang-Chul Park
article en

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.

Ajou University (KR)
Affordable and clean energy
Openalex Percentile: Top 62%
Manufacturing Process and Optimization
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Simulation Based Cycle Time Prediction for Robot Welding — Donguk Kim, Seongho Cho, et al. · (2024) | TGRS Research Map | TGRS