Welding is one of representative manufacturing processes in the industrial field. Cryogenic storage containers are also manufactured through welding, and conversion to laser welding is issue in the field due to many advantages. Since welding causes thermal-elastic deformation, design considering distortion is required. Prediction of distortion through FEM is essential, but laser welding has difficulties in the field because there is no representative heat source model. The author presented the model that can cover various models using a multi-layer heat source model in previous studies. However the previous study has a limitation which is a welding heat source model must be derived after performing bead on plate welding. Thus this study was attempted to estimate the welding heat source parameters by comparing the shape of bead under various conditions. First, the difference between penetration shape and welding heat source parameters according to welding power was analyzed. The radius of the welding heat source increased according to the welding power, and the depth of the welding heat source also increased. The correlation between the penetration shape and the welding heat source parameter appears at a similar rate, however the follow-up research is necessary with more model data.

Welding is the most widely used technology for manufacturing in the automobile, and shipbuilding industries. Fiber laser welding is rapidly introduced into the field to minimize welding distortion and fast welding speed. Although it is advantageous to use finite element analysis to predict welding distortion and find optimized welding conditions, there are various heat source model for fiber laser welding. In this study, a welding heat source was proposed using a multi-layered heat source model that encompasses most of the existing various welding heat source models: conical shape, curved model, exponential model, conical-cylindrical model, and conical-conical model. A case study was performed through finite element analysis using the radius of each layer and the ratio of heat energy of the layer as variables, and the variables were found by comparing them with the actual experimental results. For case study, by applying Adaptive simulated annealing, one of the global optimization algorithms, we were able to find the heat source model more efficiently.