Modeling and Optimization of Direct Chill Casting to Reduce Ingot Cracking

Das, S.K.; Ningileri, S.; Saito, K.; Khraisheh, M.; Hassan, M.H.; Kuwana, K.; Han, Q.; Viswanathan, S.; Sabau, Adrian S.; Clark, J.; Hyrn, J.

doi:10.2172/940314

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…To evaluate the accuracy of these predictions in predicting the quality of actual DC casting ingots, the experimental results obtained by Drezet, Wan J, Subodh, etc. [ 43 , 44 , 45 , 46 , 47 ] are used to compare with the predicted results of the predicting model in this study. Given the difficulty in obtaining accurate stress results during the casting process, the literature typically assesses the magnitude of stresses in ingots through hot tearing susceptibility [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

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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