Stochastic modelling of plasma reflection during keyhole arcwelding

Keyhole are welding (KAW), including the keyhole double-sided arc welding process being developed at the University of Kentucky and keyhole plasma are welding, can achieve much deeper narrower penetration than all other arc welding processes. If it could be controlled such that the heat input and weld pool are minimized while at the same time the desired full penetration is guaranteed, it could become an effective yet affordable technology to improve productivity in welding thick materials. However, the key in developing such a controlled KAW technology is the sensor which can detect the evolution of the keyhole. Preliminary study shows that the plasma reflection could lead to a practical yet accurate sensor. In this study, the dynamic behaviour of the plasma reflection is described using the reflection arc angle (RAA). It is found that the RAA series can be considered an autoregressive moving-average (ARMA) process. The orders of the ARMA model are determined using auto-correlation and partial auto-correlation functions. The parameters of the ARMA are recursively estimated using the extended least squares algorithm. It is found that the recursive estimates of the model parameters change as the state of the keyhole changes. A discriminator has been proposed to determine the state of the keyhole based on the recursive estimates of the model parameters.