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Shrinkage cavities and porosity are the main defects generated during the solidification process of castings. The cause of these defects is the contraction of the alloy during solidification, with the last regions to solidify not receiving effective compensation from liquid metal, resulting in cavitation defects. Shrinkage cavities and porosity significantly reduce the mechanical properties of castings and shorten their service life, thus necessitating appropriate process elimination measures. Utilizing numerical simulation technology can effectively predict the shrinkage of castings during solidification and optimize the process based on simulation results, thereby reducing the occurrence of shrinkage defects, which is a low-cost and high-efficiency method. This paper presents a machine learning-driven dynamic mesh model to simulate the dynamic shrinkage behavior of castings during solidification. Cellular automata are used to simulate the solidification process of castings, dynamically marking the displacement of boundary points and calculating the displacement of other grids using RBF neural network algorithms and support vector machine algorithms, achieving dynamic simulation of the solidification process. The model was used to simulate the shrinkage cavity morphology of the Al-4.7%Cu alloy solidification process and corresponding casting experiments were designed for verification. Comparisons between simulation and experimental results indicate that this coupled method can effectively capture the deformation of castings caused by solidification shrinkage, the evolution of complex solid-liquid interface morphologies, and the deformation of internal grids within the castings. The simulation results have an error of no more than 2% compared to experimental results, providing a new approach for numerical simulation of the solidification process.
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Keywords:
- machine learning /
- dynamic mesh /
- cellular automata /
- lattice Boltzmann
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