This study presents the first atomistic modeling investigation of Additive Friction Stir Deposition (AFSD), providing detailed insights into thermomechanical and microstructural evolution at the nanoscale. Molecular dynamics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) were employed to capture the complex interplay of rotation, translation, and frictional heating during aluminum deposition. The aluminum system was modeled using an Embedded Atom Method potential with periodic boundary conditions, enabling realistic representation of material flow and layer formation. Comprehensive atomistic diagnostics revealed that severe plastic deformation is highly localized at the tool-substrate interface, with elevated shear strain concentrated beneath the rotating feedstock. Analysis of atomic coordination numbers demonstrated significant lattice distortion in the interfacial region, while dislocation structure characterization identified defect clustering associated with plastic strain accumulation. Voronoi tessellation-based analyses quantified heterogeneous atomic packing, free-volume generation, and cavity formation, correlating spatially with regions of intense deformation. These results show that AFSD promotes metallurgical bonding through confined interfacial mixing while preserving substrate integrity. The atomistic framework developed here establishes a foundation for understanding deformation mechanisms, optimizing process parameters, and predicting microstructural evolution in solid-state additive manufacturing, offering insights complementary to experimental observations at macroscopic scales.

Preprint Link: Atomistic Modeling of Thermomechanical and Microstructural Evolution in Additive Friction Stir Deposition[v1] | Preprints.org 

GitHub Repo: Code

This Accurate prediction of temperature evolution is essential for understanding thermomechanical behavior in friction stir welding. In this study, molecular dynamics simulations were performed using LAMMPS to model aluminum friction stir welding at the atomic scale, capturing material flow, plastic deformation, and heat generation during tool plunge, traverse, and retraction. Atomic positions and velocities were extracted from simulation trajectories and transformed into physics-based two-dimensional spatial grids. These grids represent local height variation, velocity components, velocity magnitude, and atomic density, preserving spatial correlations within the weld zone. A two-dimensional convolutional neural network was developed to predict temperature directly from the spatially resolved atomistic data. Hyperparameter optimization was carried out to determine an appropriate network configuration. The trained model demonstrates strong predictive capability, achieving a coefficient of determination R2=0.9439, a root mean square error of 14.94 K, and a mean absolute error of 11.58 K on unseen test data. Class Activation Map analysis indicates that the model assigns higher importance to regions near the tool–material interface, which are associated with intense deformation and heat generation in the molecular dynamics simulations. The results show that spatial learning from atomistic simulation data can accurately reproduce temperature trends in friction stir welding while remaining consistent with physical deformation and flow mechanisms observed at the atomic scale. 

Preprint Link: Paper

GitHub Repo: Code