A deformation prediction based on deep learning for sintering process of ceramic core

doi:10.1007/s40436-025-00549-w

Advances in Manufacturing ›› 2026, Vol. 14 ›› Issue (2): 499-513.doi: 10.1007/s40436-025-00549-w

A deformation prediction based on deep learning for sintering process of ceramic core

Song-Zhe Xu, Qi-Yu Yuan, Zhi-Fan Tang, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Zhong-Ming Ren

State Key Laboratory of Advanced Special Steels, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, People's Republic of China

收稿日期:2024-06-07 修回日期:2024-07-22 出版日期:2026-04-27 发布日期:2026-04-27
通讯作者: Wei-Dong Xuan,E-mail:wdxuan@shu.edu.cn E-mail:wdxuan@shu.edu.cn
基金资助:
This work was jointly funded by the Shanghai Collaborative Innovation Project (Grant No. HCXBCY-2024-030), the National Natural Science Foundation of China (Grant No.52274386), the Shanghai Municipal Commission of Economy and Informatization (Grant No. GYQJ-2022-2-02), and the United Innovation Program of Shanghai Municipal Education Commission.

A deformation prediction based on deep learning for sintering process of ceramic core

Song-Zhe Xu, Qi-Yu Yuan, Zhi-Fan Tang, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Zhong-Ming Ren

State Key Laboratory of Advanced Special Steels, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, People's Republic of China

Received:2024-06-07 Revised:2024-07-22 Online:2026-04-27 Published:2026-04-27
Contact: Wei-Dong Xuan,E-mail:wdxuan@shu.edu.cn E-mail:wdxuan@shu.edu.cn
Supported by:
This work was jointly funded by the Shanghai Collaborative Innovation Project (Grant No. HCXBCY-2024-030), the National Natural Science Foundation of China (Grant No.52274386), the Shanghai Municipal Commission of Economy and Informatization (Grant No. GYQJ-2022-2-02), and the United Innovation Program of Shanghai Municipal Education Commission.

摘要/Abstract

摘要： Ceramic cores play a significant role in determining the cavity structure of hollow turbine blades during precision casting. However, the sintering process in preparing ceramic cores may result in shrinkage and deformation due to high temperature, presenting significant challenges in controlling the dimensional accuracy of ceramic cores. In this work, we develop a framework based on deep learning to predict the three-dimensional deformation of ceramic cores during sintering under varied sintering parameters. A finite element thermo-elasto-viscoplastic model is developed to compute sintering deformation and generate the three-dimensional deformation database. The numerical model is validated using a sintering experiment, and the maximum deviation in the deformation between the numerical and experimental results is 0.383 mm, which is 3.19% relative to the diameter of the largest inscribed circle of the ceramic core section, and satisfies the precision requirement of the third level of dimensional casting tolerance grade (DCTG3). The developed framework slices each of the three-dimensional shapes of the sintered ceramic core in sequence to obtain the two-dimensional image data for training the deep learning network. A parameter-embedded U-net network is established and trained to learn the intricate relationship between sintering parameters and deformation in sliced images. A VTK reconstruction algorithm is applied to the slice sequence to restore the predicted images from the U-net to the three-dimensional shape of the ceramic core. A metric for evaluating the model accuracy based on the error of deformation prediction (EDP) is proposed specific to the image character of ceramic core sintering, and the score of EDP for the developed U-net is 4.31%, indicating a high accuracy in predicting the sintering deformation in sliced images. An unseen combination of process parameters is numerically computed, and the entire three-dimensional deformation is compared to the prediction from the developed framework. The result shows that the relative maximum deviation in deformation is 2.93%, demonstrating the overall good performance of the developed framework in predicting sintering deformation.

The full text can be downloaded at https://doi.org/10.1007/s40436-025-00549-w

关键词: Ceramic core sintering, Deformation prediction, Deep learning, Numerical simulation

Abstract: Ceramic cores play a significant role in determining the cavity structure of hollow turbine blades during precision casting. However, the sintering process in preparing ceramic cores may result in shrinkage and deformation due to high temperature, presenting significant challenges in controlling the dimensional accuracy of ceramic cores. In this work, we develop a framework based on deep learning to predict the three-dimensional deformation of ceramic cores during sintering under varied sintering parameters. A finite element thermo-elasto-viscoplastic model is developed to compute sintering deformation and generate the three-dimensional deformation database. The numerical model is validated using a sintering experiment, and the maximum deviation in the deformation between the numerical and experimental results is 0.383 mm, which is 3.19% relative to the diameter of the largest inscribed circle of the ceramic core section, and satisfies the precision requirement of the third level of dimensional casting tolerance grade (DCTG3). The developed framework slices each of the three-dimensional shapes of the sintered ceramic core in sequence to obtain the two-dimensional image data for training the deep learning network. A parameter-embedded U-net network is established and trained to learn the intricate relationship between sintering parameters and deformation in sliced images. A VTK reconstruction algorithm is applied to the slice sequence to restore the predicted images from the U-net to the three-dimensional shape of the ceramic core. A metric for evaluating the model accuracy based on the error of deformation prediction (EDP) is proposed specific to the image character of ceramic core sintering, and the score of EDP for the developed U-net is 4.31%, indicating a high accuracy in predicting the sintering deformation in sliced images. An unseen combination of process parameters is numerically computed, and the entire three-dimensional deformation is compared to the prediction from the developed framework. The result shows that the relative maximum deviation in deformation is 2.93%, demonstrating the overall good performance of the developed framework in predicting sintering deformation.

The full text can be downloaded at https://doi.org/10.1007/s40436-025-00549-w

Key words: Ceramic core sintering, Deformation prediction, Deep learning, Numerical simulation

Song-Zhe Xu, Qi-Yu Yuan, Zhi-Fan Tang, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Zhong-Ming Ren. A deformation prediction based on deep learning for sintering process of ceramic core[J]. Advances in Manufacturing, 2026, 14(2): 499-513.

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A deformation prediction based on deep learning for sintering process of ceramic core

A deformation prediction based on deep learning for sintering process of ceramic core

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