Particle method simulation of direct-chill casting process including breakout

Tokunaga, Hitoshi; Motoyama, Yuichi; Okane, Toshimitsu

doi:10.1007/s00170-023-12610-8

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…After normalizing the casting conditions according to the boundary conditions stipulated in the model, the experimental data collected are used to calculate the predicted sump depth through Equation ( 18) and average grain radius through Equation (23). The comparison between the predicted and actual results is outlined in Table 4.…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

“…While there have been many studies focusing on optimizing the DC casting process for large-scale aluminum alloy ingots [ 21 , 22 , 23 , 24 , 25 ], there is still a lack of research on the impact of process parameters on the DC casting process under multi-field coupling. Furthermore, there are few reports on quantitative studies combined with machine learning to develop appropriate prediction models for large-scale aluminum alloy DC casting ingots.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation and Machine Learning Prediction of the Direct Chill Casting Process of Large-Scale Aluminum Ingots

Guo,

Yao,

Liu

et al. 2024

Materials

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The large-scale ingot of the 7xxx-series aluminum alloys fabricated by direct chill (DC) casting often suffers from foundry defects such as cracks and cold shut due to the formidable challenges in the precise controlling of casting parameters. In this manuscript, by using the integrated computational method combining numerical simulations with machine learning, we systematically estimated the evolution of multi-physical fields and grain structures during the solidification processes. The numerical simulation results quantified the influences of key casting parameters including pouring temperature, casting speed, primary cooling intensity, and secondary cooling water flow rate on the shape of the mushy zone, heat transport, residual stress, and grain structure of DC casting ingots. Then, based on the data of numerical simulations, we established a novel model for the relationship between casting parameters and solidification characteristics through machine learning. By comparing it with experimental measurements, the model showed reasonable accuracy in predicting the sump profile, microstructure evolution, and solidification kinetics under the complicated influences of casting parameters. The integrated computational method and predicting model could be used to efficiently and accurately determine the DC casting parameters to decrease the casting defects.

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Section: Multiple Linear Regression Analysismentioning

confidence: 99%