This article provides an overview of the methods used to control aspects of the arc welding process and research associated with the development of closed-loop feedback control of the process. Successful implementation of a closed-loop feedback control system requires sensing, modeling, and control. The article describes the commonly applied sensing techniques for arc welding control: arc sensing and nonimaging and imaging optics. It reviews the physics-based, empirically-derived, and neural network models for arc welding control. The article also discusses the research and development activities that attempt to extend the commercial, welding process controllers, namely, adaptive control, intelligent control, multivariable control, and distributed, hierarchical control.

This article lists the system parameters of the friction welding process and describes the four categories of monitoring and control of the manufacturing process. It discusses the monitoring methods of a rotary friction welded sample, for determining in-process quality of ferrous alloys, and dissimilar metals using acoustic emission. The article reviews the feasibility of detecting the presence of ferrite during microstructural evolution of friction welding of three austenitic stainless steels: 310, 304, and 255. It also explains the in-process quality control of friction welding.