Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by Industry 4.0, which aims to continuously monitor high-value manufacturing assets. This article builds upon the Industry 4.0-concept to improve the efficiency of manufacturing systems. The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding (FSW) process. It consists of a CNC manufacturing machine, sensors, edge, cloud systems, and deep neural networks, all working cohesively in real-time. The edge device, located near the FSW machine, consists of a neural network that receives sensory information and predicts weld quality in real-time. It addresses time-critical manufacturing decisions. Cloud receives the sensory data if weld quality is poor, and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine. Several experiments are conducted for training the neural networks. The framework successfully tracks process quality and improves the welding by controlling it in real-time. The system enables faster monitoring and control achieved in less than 1 second. The framework is validated through several experiments.

The article attempts to detect the defects in friction stir welding (FSW) process by analyzing the signal acquired during welding. The said welding technique utilizes pressure and heat developed by the usage of a non-consumable tool. Thus, the axial force signal carries a lot of information about the physical process, and hence, it could be used to identify the weld defects. Signal analysis has been performed by using wavelet-based techniques. Before this analysis, a methodology has been followed to select the best mother wavelets suitable for the signal. The results of defect identification have been validated by mapping the processed signal with the actual weld quality.