Stochastic modelling of plasma reflection during keyhole arcwelding

Abstract

Keyhole arc welding (KAW), including the keyhole double-sided arcwelding process being developed at the University of Kentucky and keyholeplasma arc welding, can achieve much deeper narrower penetration than allother arc welding processes. If it could be controlled such that the heatinput and weld pool are minimized while at the same time the desired fullpenetration is guaranteed, it could become an effective yet affordabletechnology to improve productivity in welding thick materials. However, thekey in developing such a controlled KAW technology is the sensor which candetect the evolution of the keyhole. Preliminary study shows that the plasmareflection could lead to a practical yet accurate sensor. In this study, thedynamic behaviour of the plasma reflection is described using the reflectionarc angle (RAA). It is found that the RAA series can be considered anautoregressive moving-average (ARMA) process. The orders of the ARMA model aredetermined using auto-correlation and partial auto-correlation functions. Theparameters of the ARMA are recursively estimated using the extended leastsquares algorithm. It is found that the recursive estimates of the modelparameters change as the state of the keyhole changes. A discriminator hasbeen proposed to determine the state of the keyhole based on the recursiveestimates of the model parameters.