Neural network modeling and system simulating for the dynamic process of varied gap pulsed GTAW with wire filler

As the base of the research work on the weld shape control during pulsed gas tungsten arc welding (GTAW) with wire filler, this paper addressed the modeling of the dynamic welding process. Topside length L_t, maximum width W_t and half-length ratio R_hl were selected to depict topside weld pool shape, and were measured on-line by vision sensing. A dynamic neural network model was constructed to predict the usually unmeasured backside width and topside height of the weld through topside shape parameters and welding parameters. The inputs of the model were the welding parameters (peak current, pulse duty ratio, welding speed, filler rate), the joint gap, the topside pool shape parameters (L_t, W_t, and R_hl), and their history values at two former pulse, a total of 24 numbers. The validating experiment results proved that the artificial neural network (ANN) model had high precision and could be used in process control. At last, with the developed dynamic model, steady and dynamic behavior was analyzed by simulation experiments, which discovered the variation rules of weld pool shape parameters under different welding parameters, and further knew well the characteristic of the welding process.