Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

doi:10.1007/s40436-024-00488-y

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 497-511.doi: 10.1007/s40436-024-00488-y

• • 上一篇

Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

Qiang-Qiang Zhai^1,2, Zhao Liu³, Ping Zhu^1,2

1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
2. National Engineering Research Center of Automotive Power and Intelligent Control, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
3. School of Design, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

收稿日期:2023-09-25 修回日期:2023-11-05 发布日期:2024-09-07
通讯作者: Zhao Liu,E-mail:hotlz@sjtu.edu.cn;Ping Zhu,E-mail:pzhu@sjtu.edu.cn E-mail:hotlz@sjtu.edu.cn;pzhu@sjtu.edu.cn
作者简介:Qiang-Qiang Zhai received the B.S. degree in Mechanical Engineering from Shandong University in 2020. Currently, he is a Ph.D. candidate at the School of Mechanical Engineering, Shanghai Jiao Tong University. His research focuses on high-pressure die casting technology, including material characterization, mechanical behavior of materials and application of artificial intelligence technology in the high-pressure die casting;
Zhao Liu is currently an associate professor in the School of Design, Shanghai Jiao Tong University. He received his Ph.D. degree from the School of Mechanical Engineering at Shanghai Jiao Tong University in 2016. His research interests include intelligent optimization theory, intelligent design theory, data-driven design, machine learning and data mining. He has published more than 40 papers in international journals and conference proceedings and obtained more than 20 Chinese national invention patents;
Ping Zhu is currently a tenured professor in the School of Mechanical Engineering, Shanghai Jiao Tong University. He received his Ph.D. degree in Mechanical Engineering from Miyazaki University in 2000. He is the deputy director of Automotive Safety Division of SAEChina and Automotive Reliability Committee of SAE-Shanghai, the senior member of CMES (Chinese Mechanical Engineering Society), and the associate editor of the Journal of Mechanical Design (JMD) of the ASME, etc. His research interests include the lightweight design and manufacture, automotive safety and reliability, big data analysis and digital twin, data-driven metamaterials design and optimization, and integrated design of material-structure-process-performance. In recent years, he has published more than 320 papers in domestic and international journals and more than 30 national invention p
基金资助:
The authors would like to acknowledge the support from the National Natural Science Foundation of China (Grant No. 52375256), and the Natural Science Foundation of Shanghai (Grant Nos. 21ZR1431500, 23ZR1431600).

Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

Qiang-Qiang Zhai^1,2, Zhao Liu³, Ping Zhu^1,2

1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
2. National Engineering Research Center of Automotive Power and Intelligent Control, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;
3. School of Design, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

Received:2023-09-25 Revised:2023-11-05 Published:2024-09-07
Contact: Zhao Liu,E-mail:hotlz@sjtu.edu.cn;Ping Zhu,E-mail:pzhu@sjtu.edu.cn E-mail:hotlz@sjtu.edu.cn;pzhu@sjtu.edu.cn
Supported by:
The authors would like to acknowledge the support from the National Natural Science Foundation of China (Grant No. 52375256), and the Natural Science Foundation of Shanghai (Grant Nos. 21ZR1431500, 23ZR1431600).

摘要/Abstract

摘要： Al-Si alloys manufactured via high-pressure die casting (HPDC) are suitable for a wide range of applications. However, the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties, thus leading to a complicated microstructure-property relationship that is difficult to capture. Hence, a computational framework incorporating machine learning and crystal plasticity method is proposed. This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure. Firstly, we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information. Subsequently, based on 160 samples obtained via the Latin hypercube sampling method, representative volume elements are constructed, and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties. Next, the yield strength, elastic modulus, strength coefficient, and strain-hardening exponent are used to characterize the stress-strain curve, and Gaussian process regression models and microstructural variables are developed. Finally, sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy. The results show that the Gaussian process regression models exhibit high accuracy (R² greater than 0.84), thus confirming the viability of the proposed method. The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties. Furthermore, the proposed framework can not only be transferred to other alloys but also be employed for material design.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00488-y

关键词: High-pressure die casting (HPDC), Machine learning, Crystal plasticity, Aluminum alloys

Abstract: Al-Si alloys manufactured via high-pressure die casting (HPDC) are suitable for a wide range of applications. However, the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties, thus leading to a complicated microstructure-property relationship that is difficult to capture. Hence, a computational framework incorporating machine learning and crystal plasticity method is proposed. This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure. Firstly, we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information. Subsequently, based on 160 samples obtained via the Latin hypercube sampling method, representative volume elements are constructed, and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties. Next, the yield strength, elastic modulus, strength coefficient, and strain-hardening exponent are used to characterize the stress-strain curve, and Gaussian process regression models and microstructural variables are developed. Finally, sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy. The results show that the Gaussian process regression models exhibit high accuracy (R² greater than 0.84), thus confirming the viability of the proposed method. The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties. Furthermore, the proposed framework can not only be transferred to other alloys but also be employed for material design.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00488-y

Key words: High-pressure die casting (HPDC), Machine learning, Crystal plasticity, Aluminum alloys

Qiang-Qiang Zhai, Zhao Liu, Ping Zhu. Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation[J]. Advances in Manufacturing, 2024, 12(3): 497-511.

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Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

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