Experimental study and 3D cellular automaton simulation of intergranular corrosion on laser-welded joints of Al-Cu-Li alloys under acidic conditions

This study proposes a multi-scale research approach that integrates micro-characterization experiments and 3D cellular automata (CA) simulations to investigate the intergranular corrosion (IGC) behavior of equiaxed grains in the fusion zone (FZ) of laser-welded joints of Al-Cu-Li alloys under acidic conditions. Combined with microscopic characterizations such as SEM and TEM, the significant segregation phenomenon of grain boundaries in the FZ of the laser welded joint of 2195-T8 aluminum-lithium alloy was revealed, and the typical morphologies of IGC in the FZ under two different concentrations of nitric acid were compared. Compared to traditional CA model, the proposed approach uses the Voronoi method combined with experimental characterization data, reconstructs a polycrystalline microstructural model. For the first time, the effects of grain boundary segregation and localized corrosion intensity on corrosion morphologies were incorporated. A CA model comprising seven cell types and five evolution rules was systematically developed, enabling the simulation of both overall uniform corrosion and localized grain boundary dissolution, as well as corrosion channel propagation in the FZ under acidic conditions. By employing neural network based parameter fitting, the model accurately captures the IGC depth evolution and the expansion characteristics of corrosion channels, thereby reproducing the local damage morphologies of the FZ after immersion corrosion. This study provides theoretical support for corrosion-resistant design of high-strength aluminum alloy welded joints and holds significant engineering value in enhancing the service life of aluminum components. • A multi-scale framework integrating micro-characterization and 3D CA simulation is proposed to predict IGC in Al-Cu-Li alloy laser-welded joints under acidic conditions. • The 3D CA model incorporates grain boundary segregation, intermetallic compounds, and weakly acidic corrosive media for the first time, enabling precise simulation of IGC morphology evolution in fusion zones. • A neural network optimization method achieves < 10% prediction error in corrosion depth, bridging empirical data and computational modeling for corrosion behavior in high-strength aluminum alloys. • Experimental validation in 3% and 10% nitric acid reveals concentration-dependent IGC mechanisms, including localized groove formation and uniform subsidence, aligning with simulation results.