Research on parameter identification of fracture model for titanium alloy under wide stress triaxiality based on machine learning

Rui Feng1, Ming-He Chen1, Ning Wang2, Lan-Sheng Xie1
1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China
2Engineering Technology Training Center, Nanjing Vocational University of Industry Technology, Nanjing, People’s Republic of China

Tóm tắt

The abilities to describe the fracture behavior and calibrate the relevant parameters are essential factors in evaluating ductile fracture criteria of titanium alloys. In this study, 14 different shapes and notched specimens were designed for uniaxial tensile and compression experiments to characterize their ductile fracture behaviors. Based on the analysis of plastic behavior and fracture mechanism, a mixed hardening model, the Von Mises yield criterion and DF2016 fracture criterion were established, respectively. A parameter-identification method based on machine learning was proposed to improve the parameter calibration of the ductile fracture model. The results showed that the DF2016 fracture model accurately predicted the damage initiation and fracture process of the forged TC4 titanium alloy during the forming process. The machine-learning method avoided extracting different stress state evolution processes and large amounts of data from the numerical model of the calibrated specimens. The combination of the semi-coupled fracture model and parameter-identification method provides a new method that alleviates the difficulty of balancing parameter calibration and the ability to characterize the ductile fracture criteria.

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Tài liệu tham khảo

Zhao YQ, Sun QY, Xin SW et al (2022) High-strength titanium alloys for aerospace engineering applications: a review on melting-forging process. Mater Sci Eng A 845:143260. https://doi.org/10.1016/j.msea.2022.143260 Shen Xh, Yao CF, Tan L et al (2023) Prediction model of surface integrity characteristics in ball end milling TC17 titanium alloy. Adv Manuf 11:541–565 Chen JY, Liu DH, Jin TY et al (2023) A novel bionic micro-textured tool with the function of directional cutting-fluid transport for cutting titanium alloy. J Mater Process Technol 311:117816. https://doi.org/10.1016/j.jmatprotec.2022.117816 Mohr D, Marcadet S (2015) Micromechanically-motivated phenomenological Hosford-Coulomb model for predicting ductile fracture initiation at low stress triaxialities. Int J Solids Struct 67/68:40–55 Weck A, Wilkinson DS, Maire E et al (2008) Visualization by X-ray tomography of void growth and coalescence leading to fracture in model materials. Acta Mater 56(12):2919–2928 Lou YS, Wu PF, Zhang C et al (2022) A stress-based shear fracture criterion considering the effect of stress triaxiality and Lode parameter. Int J Solids Struct 256:111993. https://doi.org/10.1016/j.ijsolstr.2022.111993 Zhu CX, Xu J, Yu HP et al (2022) Hybrid forming process combining electromagnetic and quasi-static forming of ultra-thin titanium sheets: formability and mechanism. Int J Mach Tool Manuf 180:103929. https://doi.org/10.1016/j.ijmachtools.2022.103929 Li FQ, Mo JH, Li JL et al (2013) Formability of Ti-6Al-4V titanium alloy sheet in magnetic pulse bulging. Mater Design 52:337–344 Matsuno T, Teodosiu C, Maeda D et al (2015) Mesoscale simulation of the early evolution of ductile fracture in dual-phase steels. Int J Plasticity 74:17–34 Cai S, Chen L (2021) Parameter identification and blanking simulations of DP1000 and Al6082-T6 using Lemaitre damage model. Adv Manuf 9:457–472 Zhang Y, Zheng J, Shen F et al (2023) Ductile fracture prediction of HPDC aluminum alloy based on a shear-modified GTN damage model. Eng Fract Mech 291(26):109541. https://doi.org/10.1016/j.engfracmech.2023.109541 Rousselier G, Luo M (2014) A fully coupled void damage and Mohr-Coulomb based ductile fracture model in the framework of a reduced texture methodology. Int J Plastic 55:1–24 Dunand M, Mohr D (2010) Hybrid experimental-numerical analysis of basic ductile fracture experiments for sheet metals. Int J Solids Struct 47:1130–1143 Sun XX, Li HW, Zhan M et al (2021) Cross-scale prediction from RVE to component. Int J Plast 140:102973. https://doi.org/10.1016/j.ijplas.2021.102973 Guo ZF, Bai RX, Lei ZK et al (2021) CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM. Eur J Mech A-Solids 90:104327. https://doi.org/10.1016/j.euromechsol.021.104327 Yao D, Pu SL, Li MY et al (2022) Parameter identification method of the semi-coupled fracture model for 6061 aluminium alloy sheet based on machine learning assistance. Int J Solids Struct 254/255:111823. https://doi.org/10.1016/j.ijsolstr.2022.111823 Baltic S, Asadzadeh MZ, Hammer P et al (2021) Machine learning assisted calibration of a ductile fracture locus model. Mater Des 203:109604. https://doi.org/10.1016/J.MATDES.2021.109604 Pandya KS, Roth CC, Mohr D (2020) Strain rate and temperature dependent fracture of aluminum alloy 7075: experiments and neural network modeling. Int J Plast 135:102788. https://doi.org/10.1016/j.ijplas.2020.102788 Wu PF, Zhang C, Lou YS et al (2023) Constitutive relationship and characterization of fracture behavior for WE43 alloy under various stress states. T Nonferr Metal Soc 33(2):438–453 Shang XQ, Cui ZS, Fu MW (2017) Dynamic recrystallization based ductile fracture modeling in hot working of metallic materials. Int J Plasticity 95:105–122 Shang XQ, Cui ZS, Fu MW (2018) A ductile fracture model considering stress state and Zener-Hollomon parameter for hot deformation of metallic materials. Int J Mech Sci 144:800–812 Qian LY, Fang G, Zeng P et al (2015) Experimental and numerical investigations into the ductile fracture during the forming of flat-rolled 5083-O aluminum alloy sheet. J Mater Process Technol 220:264–275 O’Toole L, Fang FZ (2023) Optimal tool design in micro-milling of difficult-to-machine materials. Adv Manuf 11:222–247 Cockcroft M, Latham D (1968) Ductility and the workability of metals. J Inst Metal 96(1):33–39 Brozzo P, Deluca B, Rendina R (1972) A new method for the prediction of formability in metal sheets material forming and formability. Amsterdam: IDDRG 29(2): 112–115 Oyane M, Sato T, Okimoto K et al (1980) Criteria for ductile fracture and their applications. J Mech Work Technol 4(1):65–81 Bai Y, Wierzbicki T (2008) A new model of metal plasticity and fracture with pressure and Lode dependence. Int J Plasticity 24(6):1071–1096 Lou Y, Chen L, Clausmeyer T et al (2017) Modeling of ductile fracture from shear to balanced biaxial tension for sheet metals. Inter J Solids Struct 112:169–184 Aravas N (1987) On the numerical integration of a class of pressure-dependent plasticity models. Int J Numer Meth Eng 24:1395–1416 Zhuang XC, Meng YH, Zhao Z (2018) Evaluation of prediction error resulting from using average state variables in the calibration of ductile fracture criterion. Int J Damage Mech 27(8):1231–1251 Anderson D, Butcher C, Pathak N et al (2017) Failure parameter identification and validation for a dual-phase 780 steel sheet. Int J Solids Struct 124:89–107 Shang HC, Wu PF, Lou YS et al (2022) Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy. J Mater Process Technol 302:117501. https://doi.org/10.1016/j.jmatprotec.2022.117501