Shichun Li

Real-time synchronization between physical and virtual welding is hindered by the high cost of multiphase computational fluid dynamics simulations. We propose a laser-welding digital twin (LWDT) that enables online penetration-state prediction through a Gaussian-process-parameterized Markov chain model. The LWDT integrates a physics model that supplies melt-pool and keyhole features, a data-assimilation procedure that fuses simulated keyhole areas with vision-based penetration labels, and an online-learning pipeline that continually updates transition probabilities with new process data. Initialized with 150 power–speed–thickness combinations and updated with 450 additional samples, the predictor achieves mean value error 9.48% and RMSE 9.67%, which further decrease to 4.96% and 5.14% after online learning. A 1-s sequence is generated in 0.0147 s, whereas an equivalent high-fidelity simulation requires ≈110 h, demonstrating orders-of-magnitude speedup while preserving prediction accuracy. The LWDT, thus, provides a practical route to process-level, real-time quality inference and control in laser welding.

Laser welding of dissimilar M42 high-speed steel and D6A spring steel for bi-metal band saw blades often suffers from asymmetric welds due to differing thermophysical properties. This study establishes a three-dimensional transient multiphase CFD model incorporating heat transfer, free-surface deformation, recoil pressure, and Marangoni convection to investigate the effects of welding speed on keyhole dynamics, molten pool evolution, interfacial element transport, and mechanical performance. Model predictions were validated against experimental weld morphology, elemental distribution, microhardness, and shear tests. Results reveal that welding speed governs asymmetric weld formation by controlling molten pool flow regime transitions. As speed increases, transport shifts from bottom-driven recirculation to surface-dominated flow and finally to a convection-suppressed state, altering heat and mass redistribution across the interface. Lower speeds promote elemental mixing and carbide dissolution but induce structural heterogeneity, while higher speeds restrict mixing, retaining carbide particles and steepening hardness gradients. An optimal speed of 25 mm/s balances convective mixing and thermal exposure, yielding a continuous compositional transition, uniform microstructure, and maximum shear strength. These findings link welding speed and pool dynamics to asymmetric joint performance, offering a theoretical basis for process optimization.

Large steel structures inherently have errors such as clamping and continuous thermal deformation, which complicate traditional robotic multi-layer multi-pass (MLMP) welding of medium-thick plates to ensure quality, efficiency, and universality. To address these conditions, this paper proposes an adaptive process and path planning method for MLMP welding of V-grooves utilizing line laser displacement sensors. First, dimensions and profile of the groove are obtained, feature points are extracted, and the robotic path and welding process for the root welding are planned based on the V-groove’s feature information. Then, reference process parameters is used for filling and cover welding. After each pass/layer, feature points of the V-groove will be extracted, and the previous pass/layer formation will be analyzed. And then algorithm proposed in this paper will be used to adjust the process and robotic path to ensure that, before cover welding, the average groove depth will remain within a specific range. Finally, MLMP experiments have been conducted on two V-grooves of different sizes. The results indicate that this method effectively fills the groove. Maintain the average remaining depth of the groove between 1 and 2.5 mm before cover welding, meeting the requirements of different sizes of V-grooves. The entire surface and cross-section of the weld seam are free of defects, and the fusion between the weld seam and the sidewall exceeds 1.5 mm, meeting the requirements for industrial use.

Microwave drilling (MWD) is a nontraditional machining process. To further reveal the mechanism of the MWD of metals, a physical model of the electric field during the MWD was established and mathematically solved. Microwave drilling experiments were performed, and an ultra-depth microscope was used to investigate MWD morphologies under different conditions (gap scale d and tip radius R). By comparing the characteristics of the MWD morphology and the distributions of electric field strength, the relationship between the MWD morphology characteristics and electric field strength distribution on the workpiece was revealed. The effects of the gap scale d and tip radius R on the MWD morphology and electric field strength distributions were also analyzed. The results of this study can be helpful in enhancing the understanding of the MWD mechanism and the prediction of machining accuracy and surface quality.