溶接条件をパラメータとするディープラーニングによる自動溶接ビード外観検査手法の開発と現場適用に向けた基礎検討

抄録

　This study explores the automation of welding inspection processes in shipyards by leveraging deep learning technologies to develop a system that aims to improve both efficiency and accuracy. By optimizing deep learning models in a laboratory setting, we improved the accuracy of estimating these parameters. In addition, we conducted field tests using a simple welding carriage commonly used in shipyards to validate the applicability of our system in real-world environments. The results demonstrate the potential of our approach to improve production efficiency through automated weld quality inspection. However, the study also identifies future challenges, including the need for more comprehensive training data, the incorporation of environmental data, and improvements in the estimation capabilities for various weld appearance features. This research serves as a step towards the automation of welding processes in the shipbuilding industry and provides directions for future research.