Harnessing AI deep learning model in the monitoring of welding quality

The quality of welds is a critical factor in ensuring the structural integrity and safety of manufactured components across industries such as aerospace, automotive, and construction. Traditional welding inspection methods often rely on manual or semiautomated techniques, which can be time consuming, subjective, and prone to human error. This paper explores the application of artificial intelligence (AI), specifically deep learning models, in real-time monitoring and evaluation of welding quality. By leveraging recurrent neural networks (RNNs), the proposed approach analyzes data from various sensors to detect defects. Deep learning models are trained on a dataset of 11 labeled sensor signals, allowing the system to learn complex patterns associated with both high- and low-quality welds. Experimental results demonstrate that the AI-based monitoring system can be used to automatically detect welding correctness. The integration of deep learning in welding quality control is a step towards fully automated and intelligent manufacturing processes. Furthermore, the implementation of this technology offers significant economic benefits by reducing inspection costs, minimizing rework and scrap rates, and improving overall production efficiency.