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

doi:10.1007/s40436-024-00485-1

Abstract

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.

TrendMD

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.aim.shu.edu.cn/EN/10.1007/s40436-024-00485-1

https://www.aim.shu.edu.cn/EN/Y2024/V12/I3/591

1. Ubertalli G, D'Aiuto F, Plano S et al (2016) High strain rate behavior of aluminum die cast components. Procedia Struct Integr 2:3617-3624
2. Zhang Y, Tan W, Zheng J et al (2023) Quantitative analysis of 3D pore characteristics effect on the ductility of HPDC Al-10Si-0.3 Mg alloy through X-Ray tomography. J Mater Res Technol 26:8079-8096
3. Dou K, Lordan E, Zhang Y et al (2021) A novel approach to optimize mechanical properties for aluminium alloy in high pressure die casting (HPDC) process combining experiment and modelling. J Mater Process Technol 296:117193. https://doi.org/10.1016/j.jmatprotec.2021.117193
4. Liu R, Zheng J, Godlewski L et al (2020) Influence of pore characteristics and eutectic particles on the tensile properties of Al-Si-Mn-Mg high pressure die casting alloy. Mater Sci Eng A 783:139280. https://doi.org/10.1016/j.msea.2020.139280
5. Zhang Y, Lordan E, Dou K et al (2020) Influence of porosity characteristics on the variability in mechanical properties of high pressure die casting (HPDC) AlSi7MgMn alloys. J Manuf Process 56:500-509
6. Yan P, Mao W, Fan J et al (2020) Microstructural evolution, segregation and fracture behavior of A390 alloy prepared by combined Rheo-HPDC processing and Sr-modifier. J Alloys Compd 835:155297. https://doi.org/10.1016/j.jallcom.2020.155297
7. Lin B, Fan T, Yu LH et al (2021) Microstructure and high temperature tensile properties of Al-Si-Cu-Mn-Fe alloys prepared by semi-solid thixoforming. Trans Nonferrous Met Soc China 31:2232-2249
8. Taylor JA (2012) Iron-containing intermetallic phases in Al-Si based casting alloys. Procedia Mater Sci 1:19-33
9. Faragallah OS, El-Hoseny HM, El-sayed HS (2023) Efficient brain tumor segmentation using OTSU and K-means clustering in homomorphic transform. Biomed Signal Process Control 84:104712. https://doi.org/10.1016/j.bspc.2023.104712
10. Pandey B (2023) Separating the blue cloud and the red sequence using Otsu's method for image segmentation. Astron Comput 44:100725. https://doi.org/10.1016/j.ascom.2023.100725
11. Jiang Y, Hu K, Zhang X et al (2023) A saturation channel detection method for surface defects of silicon nitride bearing rollers based on adaptive gamma correction-edge threshold segmentation coupling algorithm. Mater Today Commun 36:106397. https://doi.org/10.1016/j.mtcomm.2023.106397
12. Dong YB, Li MJ, Sun Y (2014) Research on threshold segmentation algorithms. Adv Mater Res 860/863:2888-2891
13. Wescoat E, Krugh M, Henderson A et al (2019) Vibration analysis utilizing unsupervised learning. Procedia Manuf 34:876-884
14. Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In:Proceedings of the European conference on computer vision (ECCV), Springer, Munich. pp 801-818
15. Rajamani KT, Rani P, Siebert H et al (2023) Attention-augmented U-net (AA-U-net) for semantic segmentation. Signal Image Video Process 17:981-989
16. Feng C, Zhong Y, Gao Y et al (2021) Tood:task-aligned one-stage object detection. In:2021 IEEE/CVF international conference on computer vision (ICCV), Montreal, Canada, 10-17 October
17. Bolya D, Zhou C, Xiao F et al (2019) Yolact:real-time instance segmentation. In:Proceedings of the IEEE/CVF international conference on computer vision (ICCV) Seoul, Korea, 27 October-2 November. pp 9157-9166
18. Weiler JP, Wood JT (2009) Modeling fracture properties in a die-cast AM60B magnesium alloy II-the effects of the size and location of porosity determined using finite element simulations. Mater Sci Eng A 527(1/2):32-37
19. Vanderesse N, MaireÉ, Chabod A et al (2011) Microtomographic study and finite element analysis of the porosity harmfulness in a cast aluminium alloy. Int J Fatigue 33(12):1514-1525
20. Chen H, Yang Y, Cao S et al (2021) Fatigue life prediction of aluminum alloy 6061 based on defects analysis. Int J Fatigue 147:106189. https://doi.org/10.1016/j.ijfatigue.2021.106189
21. Zhang Y, Shen F, Zheng J et al (2022) Ductility prediction of HPDC aluminum alloy using a probabilistic ductile fracture model. Theor Appl Fract Mech 119:103381. https://doi.org/10.1016/j.tafmec.2022.103381
22. Zhang W, Jing H, Xu L et al (2015) Numerical investigation of creep crack initiation in P92 steel pipes with embedded spherical defects under internal pressure at 650℃. Eng Fract Mech 139:40-55
23. Poroshin V, Shlishevsky A (2019) The forecasting of deformational and strength properties of metals with uniformly scattered defects in form of spherical hollows at single and cyclic loading. Mater Today Proc 11:58-65
24. Chan LC, Lu XZ, Yu KM (2015) Multiscale approach with RSM for stress-strain behaviour prediction of micro-void-considered metal alloy. Mater Des 83:129-137
25. Dong X, Yang H, Zhu X et al (2019) High strength and ductility aluminium alloy processed by high pressure die casting. J Alloys Compd 773:86-96
26. Sadayappan K, Birsan G, Caron F et al (2017) High pressure die casting aluminum alloys for automotive structural applications. Die Cast Eng 6(61):8-18
27. Bargmann S, Klusemann B, Markmann J et al (2018) Generation of 3D representative volume elements for heterogeneous materials:a review. Prog Mater Sci 96:322-384
28. Matouš K, Geers MGD, Kouznetsova VG et al (2017) A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials. J Comput Phys 330:192-220
29. Chen B, Peng X, Fan J et al (2005) A constitutive description for casting aluminum alloy A104 based on the analysis of cylindrical and spherical void models. Int J Plast 21:2232-2253
30. Zhang Y, Li J, Shen F et al (2022) Microstructure-property relationships in HPDC Aural-2 alloy:experimental and CP modeling approaches. Mater Sci Eng A 848:143364. https://doi.org/10.1016/j.msea.2022.143364
31. Mohr D, Treitler R (2008) Onset of fracture in high pressure die casting aluminum alloys. Eng Fract Mech 75:97-116
32. Wang Y, Dantcheva A (2020) A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes. In:15th IEEE international conference on automatic face and gesture recognition (FG 2020), Buenos Aires, Argentina, 16-20 November. pp 515-519
33. Chua LO, Roska T (1993) The CNN paradigm. IEEE Trans Circuits Syst I Fundam Theory Appl 40(3):147-156
34. Akiba T, Sano S, Yanase T et al (2019) Optuna:a next-generation hyperparameter optimization framework. In:Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery&data mining (KDD'19). Association for Computing Machinery, New York. pp 2623-2631

[1]	Yan-Ning Sun, Hai-Bo Qiao, Zeng-Gui Gao, Li-Lan Liu, Wei Qin. Machine learning-assisted surrogate model for developing MIMO quantitative relationship in plastic injection molding [J]. Advances in Manufacturing, 2026, 14(2): 514-528.
[2]	Xu-Wen Zhao, Xiao-Meng Tong, Fang-Wei Ning, Mao-Lin Cai, Fei Han, Hong-Guang Li. Review of empowering computer-aided engineering with artificial intelligence [J]. Advances in Manufacturing, 2026, 14(1): 103-143.
[3]	Mohammad Pasandidehpoor, Ana Rita Nogueira, Jo?o Mendes-Moreira, Ricardo Sousa. Survey on machine learning applied to CNC milling processes [J]. Advances in Manufacturing, 2025, 13(4): 847-885.
[4]	Le-Feng Shi, Guan-Hong Chen, Gan-Wen Chen. An AI-assistant health state evaluation method of sensing devices [J]. Advances in Manufacturing, 2025, 13(3): 539-551.
[5]	Ying-Zhang Wu, Wen-Bo Li, Yu-Jing Liu, Guan-Zhong Zeng, Cheng-Mou Li, Hua-Min Jin, Shen Li, Gang Guo. AI-enabled intelligent cockpit proactive affective interaction: middle-level feature fusion dual-branch deep learning network for driver emotion recognition [J]. Advances in Manufacturing, 2025, 13(3): 525-538.
[6]	Jian Wang, Qiu-Ren Chen, Li Huang, Chen-Di Wei, Chao Tong, Xian-Hui Wang, Qing Liu. A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints [J]. Advances in Manufacturing, 2024, 12(3): 538-555.
[7]	Chen-Di Wei, Qiu-Ren Chen, Min Chen, Li Huang, Zhong-Jie Yue, Si-Geng Li, Jian Wang, Li Chen, Chao Tong, Qing Liu. Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets [J]. Advances in Manufacturing, 2024, 12(3): 522-537.
[8]	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.
[9]	Yu-Xiang Ji, Li Huang, Qiu-Ren Chen, Charles K. S. Moy, Jing-Yi Zhang, Xiao-Ya Hu, Jian Wang, Guo-Bi Tan, Qing Liu. A machine learning-based calibration method for strength simulation of self-piercing riveted joints [J]. Advances in Manufacturing, 2024, 12(3): 465-483.
[10]	Si-Geng Li, Qiu-Ren Chen, Li Huang, Min Chen, Chen-Di Wei, Zhong-Jie Yue, Ru-Xue Liu, Chao Tong, Qing Liu. Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms [J]. Advances in Manufacturing, 2024, 12(3): 447-464.
[11]	Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren. A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing [J]. Advances in Manufacturing, 2024, 12(3): 428-446.
[12]	Si-Xiao Gao, Hui Liu, Jun Ota. Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms [J]. Advances in Manufacturing, 2024, 12(2): 227-251.
[13]	Ling-Bao Kong, Yi Yu. Precision measurement and compensation of kinematic errors for industrial robots using artifact and machine learning [J]. Advances in Manufacturing, 2022, 10(3): 397-410.
[14]	Chunling Du, Choon Lim Ho, Jacek Kaminski. Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process [J]. Advances in Manufacturing, 2021, 9(2): 206-215.
[15]	Ke-Sheng Wang Vishal S. Sharma Zhen-You Zhang. SCADA data based condition monitoring of wind turbines [J]. Advances in Manufacturing, 2014, 2(1): 61-69.

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

PDF

Knowledge

Abstract

Cite this article

share this article

Related Articles 15

Recommended Articles

Metrics

Comments