Prediction of temperature distribution around fusion zone in fiber dissimilar laser welding of AISI 304 and AISI 420: A wavelet network nonlinear ARX model

This paper has proposed a parameter estimation method for a laser welding process inherently highly nonlinear as a result of the highly nonlinear inputs and outputs of the system. Hence, a nonlinear system identification method was developed for the laser welding process using the wavelet network nonlinear autoregressive exogenous (ARX) model. The advantage of ARX over the standard nonlinear models is that it not only considers the delayed input and output regressors but also uses nonlinear functions for mapping, thus making ARX a better candidate for the prediction of nonlinear behaviors. In total, nine available datasets for the training and test phases at pulse durations, pulse frequencies, focal lengths, currents, and welding speeds were considered. Five inputs including pulse duration, pulse frequency, focal length, current and welding speed, and temperature as one output were considered. The first eight datasets were utilized for the training phase and one was used for the testing phase. The results showed that the ARX model had an acceptable performance in training and test phases, and it was capable of identifying the nonlinear and time-variant phenomenon of the laser welding process examined in this paper. For instance, most fitness values for austenitic and ferritic steel samples in the training time histories were 97.13 and 97.95, respectively.