Parameter Determination Methods for Goldak Heat Source Model in Arc Welding

The application of the double-ellipsoid heat source model proposed by Goldak is prevalent in numerical simulations of arc welding, where the selection of model parameters significantly impacts computational accuracy and efficiency. However, the absence of quantitative methods for parameter description often necessitates reliance on trial-and-error calculations or previously experience. In this work, BP neural network, GA-BP neural network and second-order polynomial regression model are employed to establish the relationship between the heat source model parameters and arc welding process parameters. The modeling process utilizes 49 datasets sourced from publicly available studies on heat source parameters, with dimensionless processing applied to the data. The results show that the three models have significant performance differences in parameter estimation. Notably, the GA-BP neural network model demonstrates excellent predictive ability, as evidenced by an RMSE value of 0.09. The approach presented in this work is capable of precisely and effectively determining Goldak heat source parameters based on any given welding process parameters, with significant potential to improve the accuracy and computational efficiency of numerical simulations in arc welding.