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

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.

Keywords

Artificial intelligence (AI) / Properties prediction / High-pressure die-casting (HPDC) / Image recognition / Machine learning

References

[1.] Ubertalli G, D’Aiuto F, Plano S, et al. High strain rate behavior of aluminum die cast components. Procedia Struct Integr, 2016, 2: 3617-3624.

[2.] Zhang Y, Tan W, Zheng J, et al. 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, 2023, 26: 8079-8096.

[3.] Dou K, Lordan E, Zhang Y, et al. 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, 2021, 296.

[4.] Liu R, Zheng J, Godlewski L, et al. 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, 2020, 783.

[5.] Zhang Y, Lordan E, Dou K, et al. Influence of porosity characteristics on the variability in mechanical properties of high pressure die casting (HPDC) AlSi7MgMn alloys. J Manuf Process, 2020, 56: 500-509.

[6.] Yan P, Mao W, Fan J, et al. Microstructural evolution, segregation and fracture behavior of A390 alloy prepared by combined Rheo-HPDC processing and Sr-modifier. J Alloys Compd, 2020, 835.

[7.] Bo LIN, Tao FAN, Hy Li, et al. Microstructure and high temperature tensile properties of Al-Si-Cu-Mn-Fe alloys prepared by semi-solid thixoforming. Trans Nonferrous Met Soc China, 2021, 31: 2232-2249.

[8.] Taylor JA. Iron-containing intermetallic phases in Al-Si based casting alloys. Procedia Mater Sci, 2012, 1: 19-33.

[9.] Faragallah OS, El-Hoseny HM, El-sayed HS. Efficient brain tumor segmentation using OTSU and K-means clustering in homomorphic transform. Biomed Signal Process Control, 2023, 84.

[10.] Pandey B. Separating the blue cloud and the red sequence using Otsu’s method for image segmentation. Astron Comput, 2023, 44.

[11.] Jiang Y, Hu K, Zhang X, et al. 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, 2023, 36.

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

[13.] Wescoat E, Krugh M, Henderson A, et al. Vibration analysis utilizing unsupervised learning. Procedia Manuf, 2019, 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. Attention-augmented U-Net (AA-U-Net) for semantic segmentation. Signal Image Video Process, 2023, 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. 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, 2009, 527(1/2): 32-37.

[19.] Vanderesse N, Maire É, Chabod A, et al. Microtomographic study and finite element analysis of the porosity harmfulness in a cast aluminium alloy. Int J Fatigue, 2011, 33(12): 1514-1525.

[20.] Chen H, Yang Y, Cao S, et al. Fatigue life prediction of aluminum alloy 6061 based on defects analysis. Int J Fatigue, 2021, 147.

[21.] Zhang Y, Shen F, Zheng J, et al. Ductility prediction of HPDC aluminum alloy using a probabilistic ductile fracture model. Theor Appl Fract Mech, 2022, 119.

[22.] Zhang W, Jing H, Xu L, et al. Numerical investigation of creep crack initiation in P92 steel pipes with embedded spherical defects under internal pressure at 650 °C. Eng Fract Mech, 2015, 139: 40-55.

[23.] Poroshin V, Shlishevsky A. 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, 2019, 11: 58-65.

[24.] Chan LC, Lu XZ, Yu KM. Multiscale approach with RSM for stress-strain behaviour prediction of micro-void-considered metal alloy. Mater Des, 2015, 83: 129-137.

[25.] Dong X, Yang H, Zhu X, et al. High strength and ductility aluminium alloy processed by high pressure die casting. J Alloys Compd, 2019, 773: 86-96.

[26.] Sadayappan K, Birsan G, Caron F, et al. High pressure die casting aluminum alloys for automotive structural applications. Die Cast Eng, 2017, 6(61): 8-18.

[27.] Bargmann S, Klusemann B, Markmann J, et al. Generation of 3D representative volume elements for heterogeneous materials: a review. Prog Mater Sci, 2018, 96: 322-384.

[28.] Matouš K, Geers MGD, Kouznetsova VG, et al. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials. J Comput Phys, 2017, 330: 192-220.

[29.] Chen B, Peng X, Fan J, et al. A constitutive description for casting aluminum alloy A104 based on the analysis of cylindrical and spherical void models. Int J Plast, 2005, 21: 2232-2253.

[30.] Zhang Y, Li J, Shen F, et al. Microstructure-property relationships in HPDC Aural-2 alloy: experimental and CP modeling approaches. Mater Sci Eng A, 2022, 848.

[31.] Mohr D, Treitler R. Onset of fracture in high pressure die casting aluminum alloys. Eng Fract Mech, 2008, 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. The CNN paradigm. IEEE Trans Circuits Syst I Fundam Theory Appl, 1993, 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

Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52271019)