Abstract
Unlike the application of machine vision in many other fields, there is a particular problem in developing machine vision for automated and robotic welding processes, because the disturbance of the arc light deteriorates the field to be viewed. This paper describes an analysis of the radiated energy from the weld pool, and based on this describes how vision sensing of the welding region may be improved.
An approach using specified pattern parameters is described to evaluate the ability to recognize an acquired image under different conditions such as with vidicons or sensor devices, different welding variables and optical parameters. Based on analysis of radiation from the welding region, a satisfactory wavelength range for sensing the image of the weld region is proposed and proved by experimentation. Images of gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) regions have been modelled and successfully implemented for seam tracking control with a simultaneously developed low-cost vision system.