The Improvement concerning Recognition Accuracy of Six Skill Levels of Flux-Cored Arc Welding Hand Welder using Convolutional Neural Network-Long Short-Term Memory

The shipbuilding industry in developing countries is a sector that heavily relies on manual welding methods of Shielded Metal Arc Welding (SMAW) and Flux-Cored Arc Welding (FCAW). The reliance on manual welding skills often leads to substantial rework due to inconsistencies in weld quality and variations in welder proficiency. There is an observation that many welders drop performance due to fatigue problems after a period of working or changes in working conditions. Therefore, this study aimed to develop an Artificial Intelligence (AI)-driven to monitor the changes concerning the performance of welder using wearable sensors and a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in improving recognition accuracy of six FCAW welder performance levels. Data of six welder hand movements were collected during 1G, 2G, and 3G positional welding and analyzed using CNN-LSTM. During the analysis, the hand movement data of six levels of welder skills were classified by the number of discontinuity records. The model achieved a total accuracy exceeding 95%, showing its effectiveness in skill assessment and real-time welder monitoring. These results show the potential of AI-powered systems to improve welding productivity and reduce project delays in shipbuilding.