Skip to main content
SpringerLink
Log in

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

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  8. Taylor JA (2012) Iron-containing intermetallic phases in Al-Si based casting alloys. Procedia Mater Sci 1:19–33

    Article  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  ADS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  12. Dong YB, Li MJ, Sun Y (2014) Research on threshold segmentation algorithms. Adv Mater Res 860/863:2888–2891

    Google Scholar 

  13. Wescoat E, Krugh M, Henderson A et al (2019) Vibration analysis utilizing unsupervised learning. Procedia Manuf 34:876–884

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  19. Vanderesse N, Maire É, 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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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 °C. Eng Fract Mech 139:40–55

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  ADS  MathSciNet  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  31. Mohr D, Treitler R (2008) Onset of fracture in high pressure die casting aluminum alloys. Eng Fract Mech 75:97–116

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

Download references

Acknowledgments

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 51575068, 51501023, and 52271019).

Author information

Authors and Affiliations

  1. Key Laboratory for Light-Weight Materials, Nanjing Tech University, Nanjing, 211816, People’s Republic of China

    Yu-Tong Yang & Shi-Yao Huang

  2. Xi’an Jiaotong-Liverpool University, Suzhou, 215000, Jiangsu, People’s Republic of China

    Yu-Tong Yang, Zhen Zheng, Liang-Xi Pu & Ding-Ding Chen

  3. Materials Academy JITRI, Suzhou, 215100, Jiangsu, People’s Republic of China

    Yu-Tong Yang, Zhong-Yuan Qiu, Zhen Zheng, Liang-Xi Pu, Ding-Ding Chen & Shi-Yao Huang

  4. Key Laboratory for Light-weight Materials, Nanjing University of Science and Technology, Nanjing, 210009, People’s Republic of China

    Zhong-Yuan Qiu

  5. College of Materials Science and Engineering, Chongqing University, Chongqing, 400044, People’s Republic of China

    Jiang Zheng

  6. Collaborate Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, People’s Republic of China

    Rui-Jie Zhang

  7. Chongqing Millison Technologies Inc., Chongqing, 401321, People’s Republic of China

    Bo Zhang

Authors
  1. Yu-Tong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-Yuan Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang-Xi Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ding-Ding Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rui-Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shi-Yao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiang Zheng or Shi-Yao Huang.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, YT., Qiu, ZY., Zheng, Z. et al. Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy. Adv. Manuf. (2024). https://doi.org/10.1007/s40436-024-00485-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40436-024-00485-1

Keywords

Search

Navigation