Using the numerical simulation and artificial neural network (ANN) to evaluate temperature distribution in pulsed laser welding of different alloys

The temperature field during laser welding process plays an important role on determining the quality and quantity of the weld bead size, microstructure characterizations and mechanical properties of the welding interface in the thermal engineering applications. In this study, using the numerical simulation, the influence of pulse duration and frequency on the temperature distribution and velocity field in distinctive laser welding of stainless steel 420 (S.S 420)/stainless steel 304 (S.S 304), and Bohler 303 (B 303)/stainless steel 304 (S.S 304) was examined. The results of numerical modeling illustrated that shear stress of Marangoni and buoyancy force are the most curtail aspects in the formation of the flow of liquid metal. A novel artificial intelligence method is proposed to optimally predict the melting ratio, and maximum temperature of the materials. To this end, a combination of ANN and Particle Swarm Optimization (PSO) algorithms are employed. The PSO algorithm is used to optimize the architecture and training algorithm of the ANN, while the ANN is employed for the regression problem. Based on the results, a three-layer feed-forward architecture with sigmoid transfer functions having 17 and 8 neurons in the hidden layers combined with the scaled conjugate gradient backpropagation training scheme is recognized by the PSO as the optimal configuration. Application of optimal ANN to the regression problem results in an acceptable level of error for the training, validation, and test datasets. Finally, the optimized ANN can be utilized to anticipate the melting ratio and thereby the resultant temperature.