This review is on the various techniques and methodologies applied to sensor based
monitoring of the quality and control of defects in an advanced joining process named …

The fourth industrial revolution, Industry 4.0, has brought internet, artificial intelligence (AI),
and machine learning (ML) concepts into manufacturing. There is an immediate need to …

In the current scenario, industries need to have continuous improvement in their
manufacturing processes. Digital twin (DT), a virtual representation of a physical entity …

This study proposes a strategy for developing force-data-driven machine learning models to
precisely predict defects and their types in friction stir welding (FSW). The characteristics of …

In the current research, a coupled Eulerian and Lagrangian method is used to model the
friction stir welding process. Volume of fluid principle is used to predict the formation of …

In the present work, a novel cloud-based remote and real time monitoring and control
scheme has been developed for a manufacturing process named friction stir welding (FSW) …

In this study, the response of welding force characteristic to weld-forming process in friction
stir welding (FSW) was systematically investigated. Assisted by machine learning technique …

Friction stir welding and processing enabled the creation of stronger joints, novel ultrafine-
grained metals, new metal matrix composites, and multifunctional surfaces at user-defined …

An analysis of the evolution of the torque and of the temperature with welding conditions, in
Friction Stir Welding (FSW) of aluminium alloys, was conducted. More precisely, torque and …

Aluminum alloy is the main structure material in aerospace industry. Online defect detection
for aluminum alloy in pulsed gas tungsten arc welding (GTAW) is still challenging, especially …