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<p>To address the microstructural heterogeneity in long-flow, thin-walled Al-Si-Cu alloy die castings under extreme coupled thermo-mechanical-fluid conditions, this research develops a fully coupled four-field predictive model. The methodology integrates temperature, velocity, pressure, and microstructure (TVPM) to quantify the bimodal evolution of coarse externally solidified crystals and fine nucleated new grains. Experimental and simulation results reveal a U-shaped evolution pattern for the average grain size along the filling path, initially refining from 13.82 μm to 11.58 μm before coarsening to 12.33 μm at the distal end. The established model demonstrated high accuracy with a relative error of less than 4% across extreme wall thickness variations. Furthermore, implementing a low-temperature and fast flow process optimization strategy successfully mitigated microstructural disparity. This intervention reduced the average grain size dispersion by 29.9% to a narrow range of 1.57 μm. Additionally, the average tensile strength improved by 32.5 MPa to reach 262.33 MPa, while the spatial variation in tensile strength was reduced to 8.43 MPa.</p>

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Prediction Model of Microstructure Evolution for Long-Flow Thin-Walled Al-Si-Cu Alloy Die Casting Components

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Yucheng Chen,
  • Bing Li,
  • Zehui Xu,
  • Yongheng Zhang,
  • Renguo Guan

摘要

To address the microstructural heterogeneity in long-flow, thin-walled Al-Si-Cu alloy die castings under extreme coupled thermo-mechanical-fluid conditions, this research develops a fully coupled four-field predictive model. The methodology integrates temperature, velocity, pressure, and microstructure (TVPM) to quantify the bimodal evolution of coarse externally solidified crystals and fine nucleated new grains. Experimental and simulation results reveal a U-shaped evolution pattern for the average grain size along the filling path, initially refining from 13.82 μm to 11.58 μm before coarsening to 12.33 μm at the distal end. The established model demonstrated high accuracy with a relative error of less than 4% across extreme wall thickness variations. Furthermore, implementing a low-temperature and fast flow process optimization strategy successfully mitigated microstructural disparity. This intervention reduced the average grain size dispersion by 29.9% to a narrow range of 1.57 μm. Additionally, the average tensile strength improved by 32.5 MPa to reach 262.33 MPa, while the spatial variation in tensile strength was reduced to 8.43 MPa.