Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

doi:10.1007/s40436-024-00485-1

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 591-602.doi: 10.1007/s40436-024-00485-1

Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

Yu-Tong Yang^1,2,3, Zhong-Yuan Qiu^3,4, Zhen Zheng^2,3, Liang-Xi Pu^2,3, Ding-Ding Chen^2,3, Jiang Zheng⁵, Rui-Jie Zhang⁶, Bo Zhang⁷, Shi-Yao Huang^1,3

1. Key Laboratory for Light-Weight Materials, Nanjing Tech University, Nanjing, 211816, People's Republic of China;
2. Xi'an Jiaotong-Liverpool University, Suzhou, 215000, Jiangsu, People's Republic of China;
3. Materials Academy JITRI, Suzhou, 215100, Jiangsu, People's Republic of China;
4. Key Laboratory for Light-weight Materials, Nanjing University of Science and Technology, Nanjing, 210009, People's Republic of China;
5. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, People's Republic of China;
6. Collaborate Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
7. Chongqing Millison Technologies Inc., Chongqing, 401321, People's Republic of China

收稿日期:2023-10-01 修回日期:2023-11-03 发布日期:2024-09-07
通讯作者: Jiang Zheng,E-mail:jzheng@cqu.edu.cn;Shi-Yao Huang,E-mail:huangsy@njtech.edu.cn E-mail:jzheng@cqu.edu.cn;huangsy@njtech.edu.cn
作者简介:Yu-Tong Yang He is currently pursuing a Ph.D. degree at Xi’an Jiaotong-Liverpool University, specializing in alloy design, microstructure, and mechanical properties of HPDC aluminum alloys;
Zhong-Yuan Qiu He is currently pursuing a master’s degree at Nanjing University of Science and Technology. His research interests include performance prediction of casting aluminium alloy;
Zhen Zheng He is currently pursuing a Ph.D. degree at Xi’an Jiaotong-Liverpool University. His research interest is the prediction of mechanical properties of HPDC Al-Si alloys;
Liang-Xi Pu He received Bachelor degree in Software Engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2022. He is currently pursuing a master’s degree at Xi’an Jiaotong-Liverpool University. His research interests include AI image processing and intelligent system;
Ding-Ding Chen He is currently pursuing a master’s degree at Xi’an Jiaotong-Liverpool University. His research interests include deep learning and data mining;
Jiang Zheng He is currently an Associate Professor with Chongqing University, Chongqing, China. His research interests include plastic deformation mechanisms and performance control of light alloys;
Rui-Jie Zhang Received Ph.D. degree in materials processing technology from Northwestern Polytechnical University, Xi’an, China, in 2004. He is currently an Associate Professor with University of Science and Technology Beijing, Beijing, China. His research interests include materials microstructure simulation and properties prediction;
Bo Zhang He received a master’s degree from Chongqing University, in 2009. Currently he holds the position of Vice General Manager at Millison Technology Co., Ltd. His research interests include HPDC of light alloys, mechanical design and manufacturing, as well as system design and development;
Shi-Yao Huang He received a Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China. He is currently a researcher at the Materials Academy of JITRI. His research interests include materials microstr ucture and proper ties prediction.
基金资助:
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 51575068, 51501023, and 52271019).

Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

Yu-Tong Yang^1,2,3, Zhong-Yuan Qiu^3,4, Zhen Zheng^2,3, Liang-Xi Pu^2,3, Ding-Ding Chen^2,3, Jiang Zheng⁵, Rui-Jie Zhang⁶, Bo Zhang⁷, Shi-Yao Huang^1,3

1. Key Laboratory for Light-Weight Materials, Nanjing Tech University, Nanjing, 211816, People's Republic of China;
2. Xi'an Jiaotong-Liverpool University, Suzhou, 215000, Jiangsu, People's Republic of China;
3. Materials Academy JITRI, Suzhou, 215100, Jiangsu, People's Republic of China;
4. Key Laboratory for Light-weight Materials, Nanjing University of Science and Technology, Nanjing, 210009, People's Republic of China;
5. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, People's Republic of China;
6. Collaborate Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China;
7. Chongqing Millison Technologies Inc., Chongqing, 401321, People's Republic of China

Received:2023-10-01 Revised:2023-11-03 Published:2024-09-07
Contact: Jiang Zheng,E-mail:jzheng@cqu.edu.cn;Shi-Yao Huang,E-mail:huangsy@njtech.edu.cn E-mail:jzheng@cqu.edu.cn;huangsy@njtech.edu.cn
Supported by:
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 51575068, 51501023, and 52271019).

摘要/Abstract

摘要： High-pressure die casting (HPDC) is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation. However, the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation. Therefore, a methodology for property prediction must be developed. Material characterization, simulation technologies, and artificial intelligence (AI) algorithms were employed. Firstly, an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy. Moreover, a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results. Additionally, the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model, allowing accurate prediction of the property distribution of the HPDC Al-Si alloy. The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00485-1

关键词: Artificial intelligence (AI), Properties prediction, High-pressure die-casting (HPDC), Image recognition, Machine learning

Abstract: High-pressure die casting (HPDC) is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation. However, the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation. Therefore, a methodology for property prediction must be developed. Material characterization, simulation technologies, and artificial intelligence (AI) algorithms were employed. Firstly, an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy. Moreover, a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results. Additionally, the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model, allowing accurate prediction of the property distribution of the HPDC Al-Si alloy. The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00485-1

Key words: Artificial intelligence (AI), Properties prediction, High-pressure die-casting (HPDC), Image recognition, Machine learning

Yu-Tong Yang, Zhong-Yuan Qiu, Zhen Zheng, Liang-Xi Pu, Ding-Ding Chen, Jiang Zheng, Rui-Jie Zhang, Bo Zhang, Shi-Yao Huang. Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy[J]. Advances in Manufacturing, 2024, 12(3): 591-602.

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Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

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