Friction Stir Welding (FSW) is widely used for high-strength aluminum alloys due to its solid-state bonding, which ensures superior weld quality and service stability. However, thermo-mechanical interactions during welding can induce complex residual stress distributions, compromising joint integrity. Previous studies have primarily focused on thermal load-driven stress evolution, often neglecting mechanical factors such as the shear force generated by the stirring pin. This study develops a three-dimensional thermo-mechanical coupled finite element model based on a moving heat source. The model incorporates axial pressure from the tool shoulder and torque-derived shear force from the stirring pin. A hybrid surface–volumetric heat source is applied to represent frictional heating, and realistic mechanical boundary conditions are introduced to reflect actual welding conditions. Simulations on AA6061-T6 aluminum alloy show that under stable welding, the peak temperature in the weld zone reaches approximately 453 °C. Residual stress analysis indicates a longitudinal tensile peak of ~170 MPa under thermal loading alone, which reduces to ~150 MPa when mechanical loads are included, forming a characteristic M-shaped distribution. Further comparison with a Coupled Eulerian–Lagrangian (CEL) model reveals stress asymmetry, with higher tensile stress on the advancing side. This is primarily attributed to the directional shear force, which promotes greater plastic deformation on the advancing side than on the retreating side. The consistency between the proposed model and CEL results confirms its validity. This study provides a reliable framework for residual stress prediction in FSW and supports process parameter optimization.

Selective laser melting is a commonly employed additive manufacturing technique that facilitates the fabrication of intricate geometries through the laser-induced melting of powder materials. The quality of the produced parts is significantly influenced by the molten pool morphology, which is affected by parameters such as laser power, scanning rate, and powder characteristics. However, the selection of unknown parameters within the heat source model significantly impacts the simulation outcomes and must be carefully considered. This study addresses this issue by proposing an inversion method for accurately determining the parameters of the Goldak double ellipsoid heat source model using molten pool morphology as a reference. A pattern search algorithm combined with Bayesian inference was employed to invert and estimate the heat source parameters. The results demonstrated that the inversed parameters significantly improved the prediction accuracy of molten pool geometry. The inverse parameters χ₀, χ₁, and χ₂ were 1.17, 1.00, and 2.08, respectively. The study provides valuable insights into the use of image-based methods for parameter inversion and offers a more reliable tool for improving the precision of simulations. These findings have important implications for optimizing processing conditions and enhancing the overall quality of additively-manufactured components.

Heat source parameters are critical input variables in welding thermal analysis, directly and significantly affecting the accuracy of the temperature field distribution, welding distortion, and residual stress prediction. This is particularly important in safety-critical welded structures, where high-precision heat source parameter identification is essential for ensuring the thermal simulation accuracy and mechanical performance reliability. Traditional parameter identification methods based on finite element simulations or experiments have limitations in adapting to complex working conditions and variable environments. To address this, this paper proposes the Heat Source Parameter Identification Network (HSPINet) model based on a residual convolutional neural network (ResNet) architecture with an attention mechanism capable of extracting key features from the weld morphology of T-joint structures, while accounting for the influence of process parameters and joint dimensions to achieve efficient and accurate identification of heat source parameters. This study not only enhances the intelligence level of heat source parameter identification but also provides a practical, intelligent tool for welding simulation and thermal field evaluation in complex industrial applications, demonstrating significant theoretical value and broad applicability in laser processing and manufacturing scenarios.