Artificial neural network enhanced plasticity modeling and ductile fracture characterization of grade-1 commercial pure titanium

IF 9.4 1区 材料科学 Q1 ENGINEERING, MECHANICAL International Journal of Plasticity Pub Date : 2024-06-21 DOI:10.1016/j.ijplas.2024.104044
Abrar Salam Ebrahim , Qi Zhang , Jinjin Ha
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Abstract

This study primarily aims to develop a robust modeling approach to capture the complex material behavior of CP-Ti, appeared by high anisotropy, differential hardening due to anisotropy evolution, and flow behavior sensitive to strain rate and temperature, using artificial neural networks (ANNs). Plasticity is characterized by uniaxial tension and in-plane biaxial tension tests at temperatures of 0 °C and 20 °C with strain rates of 0.001 /s and 0.01 /s, and the results are used to calibrate the non-quadratic anisotropic Yld2000–3d yield function with respect to the plastic work. In order to predict the intricate plastic deformation with the temperature and strain rate effects, two distinct ANN models are developed; one to capture the strain hardening behavior and the other to predict the anisotropic parameters in the chosen yield function. The developed ANN models predict an unseen dataset well, which is intermediate testing conditions at a temperature of 10 °C and strain rate of 0.005 /s. The ANN models, being computationally stable and adhering to conventional constitutive equations, are implemented into a user material subroutine for the ductile fracture characterization of CP-Ti sheet using the hybrid experimental-numerical analysis. The favorable agreement between experimental data and numerical predictions, particularly using the ANN models with evolving anisotropic material parameters for the Yld2000–3d yield function, underscores the significance of the differential hardening effect on the ductile fracture behavior and highlights the capabilities of ANN models to capture the complex plastic behavior of CP-Ti. The key parameters including stress triaxiality, Lode angle parameter, and equivalent plastic strain at the fracture location are extracted from the simulations, enabling the calibration of ductile fracture models, namely Johnson-Cook, Hosford-Coulomb, and Lou-2014, and construction of fracture envelopes.

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人工神经网络增强型塑性建模和 1 级商用纯钛的韧性断裂表征
本研究的主要目的是利用人工神经网络(ANN)开发一种稳健的建模方法,以捕捉 CP-Ti 的复杂材料行为(表现为高各向异性、各向异性演变导致的差异硬化以及对应变率和温度敏感的流动行为)。在 0 °C 和 20 °C 温度条件下,以 0.001 /s 和 0.01 /s 的应变率进行单轴拉伸和平面双轴拉伸试验,对塑性进行表征,并利用试验结果校准与塑性功有关的非二次方各向异性 Yld2000-3d 屈服函数。为了预测具有温度和应变率效应的复杂塑性变形,开发了两个不同的 ANN 模型:一个用于捕捉应变硬化行为,另一个用于预测所选屈服函数中的各向异性参数。所开发的 ANN 模型能很好地预测未见数据集,即温度为 10 °C、应变率为 0.005 /s 的中间测试条件。ANN 模型具有计算稳定性,并与传统的构成方程保持一致,因此被应用到用户材料子程序中,利用实验-数值混合分析法对 CP-Ti 板材进行韧性断裂表征。实验数据与数值预测之间的良好一致性,特别是使用 Yld2000-3d 屈服函数的各向异性材料参数演化的 ANN 模型,强调了差分硬化效应对韧性断裂行为的重要意义,并突出了 ANN 模型捕捉 CP-Ti 复杂塑性行为的能力。从模拟中提取了关键参数,包括应力三轴性、Lode 角参数和断裂位置的等效塑性应变,从而校准了韧性断裂模型,即 Johnson-Cook 模型、Hosford-Coulomb 模型和 Lou-2014 模型,并构建了断裂包络线。
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来源期刊
International Journal of Plasticity
International Journal of Plasticity 工程技术-材料科学:综合
CiteScore
15.30
自引率
26.50%
发文量
256
审稿时长
46 days
期刊介绍: International Journal of Plasticity aims to present original research encompassing all facets of plastic deformation, damage, and fracture behavior in both isotropic and anisotropic solids. This includes exploring the thermodynamics of plasticity and fracture, continuum theory, and macroscopic as well as microscopic phenomena. Topics of interest span the plastic behavior of single crystals and polycrystalline metals, ceramics, rocks, soils, composites, nanocrystalline and microelectronics materials, shape memory alloys, ferroelectric ceramics, thin films, and polymers. Additionally, the journal covers plasticity aspects of failure and fracture mechanics. Contributions involving significant experimental, numerical, or theoretical advancements that enhance the understanding of the plastic behavior of solids are particularly valued. Papers addressing the modeling of finite nonlinear elastic deformation, bearing similarities to the modeling of plastic deformation, are also welcomed.
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