Abstract

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.

中文翻译：

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基于机器学习的宽应力三轴钛合金断裂模型参数辨识研究

摘要

描述断裂行为和标定相关参数的能力是评估钛合金韧性断裂准则的重要因素。在这项研究中，设计了 14 个不同形状和缺口的样本进行单轴拉伸和压缩实验，以表征其延性断裂行为。在塑性行为和断裂机制分析的基础上，分别建立了混合硬化模型、Von Mises屈服准则和DF2016断裂准则。提出一种基于机器学习的参数识别方法来改进韧性断裂模型的参数标定。结果表明，DF2016断裂模型准确预测了锻造TC4钛合金成形过程中的损伤萌生和断裂过程。机器学习方法避免了从标定试件的数值模型中提取不同的应力状态演化过程和大量数据。半耦合断裂模型与参数识别方法的结合提供了一种新方法，减轻了平衡参数校准的难度和表征延性断裂准则的能力。