Background

Ratcheting is an important mechanical behavior of metals and alloys, which is caused by the repeated accumulations of tensile and compressive strain in circle load. However, the current characterization methods of ratcheting effect are mostly based on standardized testing and uniform data, and more comprehensive field measurement data cannot be used.

Objective

This paper focuses on how to make full use of field measurement data to characterize ratcheting effect and identify the corresponding kinematic constitutive.

Methods

A nonlinear virtual field method that can invert the parameters of Chaboche constitutive from strain field data is proposed. And a return mapping strategy driven by iteration of internal variables is used to reconstruct the stress field, which ensures the convergence speed and global convergence in the black box search of the nonlinear virtual field method.

Results

By using the finite-element model to generate the strain field data, the numerical experiment shows that the ratchet path identified by the nonlinear virtual field algorithm is basically consistent with the prior ratchet path generated by the finite-element simulation. The adaptability of the algorithm to data density and noise amplitude was also verified: under lower data noise interference, more strain field training data makes the inversion results more accurate; but in the case of high sound amplitude, it is necessary to reduce the data size to obtain accurate fitting results of the ratchet path.

Conclusions

By training the measured field data from 3D-digital image correlation, it is shown that the algorithm can also run effectively under the complex working conditions of non-uniform deformation.

中文翻译：

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循环载荷下棘轮效应的虚拟场表征

背景

棘轮传动是金属和合金的重要机械性能，这是由于循环载荷中反复出现拉伸和压缩应变累积而引起的。但是，目前棘轮效应的表征方法主要是基于标准化测试和统一数据，无法使用更全面的现场测量数据。

客观的

本文着重于如何充分利用现场测量数据来表征棘轮效应并确定相应的运动本构。

方法

提出了一种非线性虚拟场方法，该方法可以根据应变场数据反演Chaboche本构参数。并采用内部变量迭代驱动的返回映射策略重构应力场，从而保证了非线性虚拟场法黑盒搜索的收敛速度和全局收敛性。

结果

通过有限元模型生成应变场数据，数值实验表明，非线性虚拟场算法确定的棘轮路径与有限元模拟生成的先行棘轮路径基本一致。验证了该算法对数据密度和噪声幅度的适应性：在较低的数据噪声干扰下，更多的应变场训练数据使反演结果更加准确。但是在高声音振幅的情况下，有必要减小数据大小以获得棘轮路径的准确拟合结果。

结论

通过训练来自3D数字图像相关性的实测数据，表明该算法在非均匀变形的复杂工作条件下也能有效运行。