Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917169340612608 |
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| author | Mishra, Akshansh |
| author_facet | Mishra, Akshansh |
| contents | 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 R square of 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_21344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding Mishra, Akshansh Materials Science Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning 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 R square of 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. |
| title | Atomistic Simulation Guided Convolutional Neural Networks for Thermal Modeling of Friction Stir Welding |
| topic | Materials Science Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2512.21344 |