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谢谢你，Ben。我大体上同意你的观点，尽管我对2D DIC的使用有点怀疑。当然，在响应的弹性部分，不可避免的离面运动使得几乎不可能获得鲁棒弹性常数。已经发表了一些技巧来克服这个问题，但根据我的看法，需要额外的努力意味着立体dic仍然是最佳选择。就成本而言，额外的相机不是问题，校准所需的额外时间也不是决定因素。对于斑点，我认为我们应该远离喷漆，因为这不能保证高质量的测量，并且给不确定性量化增加了随机因素，例如会妨碍标准化。就像使用一个随机变化的应变计。
Your comment about the level of expertise is interesting, and I certainly agree to part of it. Let me explain. If we are talking about routine tests for, say, material characterisation, including quality control, we need standards that could be followed by lab technicians with a detailed operational roadmap. On the other hand, outside of this framework, the complexity of DIC is akin to that of finite element simulation and handling this without thorough training and understanding of the theory is a recipe for disaster. As for numerical simulation, DIC brings a lot of information but there is a price to pay in terms of training.
As for data inversion, there is scope for progress indeed, but the current tools (FE updating, VFM) are already sufficiently mature to establish new MT2.0test configurations now. Regardless of the technical nitty-gritty, I think a vast space of improvement is in the very formulation of constitutive material models. For anisotropic plasticity for instance, models for yield surfaces are closed, i.e., the model predicts the behaviour for any combination of stress components. This is because the identification based on statically determinate tests only puts one point in the stress space for each test. With MT2.0, we have a point cloud, so it would be possible to formulate a yield surface using B-spline interpolation for instance, which would provide very robust data in the space activated by the test. But this would not provide any information elsewhere. This may be seen as a weakness but I think it is a strength as if you want data in a particular part of the stress space, you should perform a test providing data there. Uniaxial tests do not tell you anything about a biaxial stress state. Extrapolation from a certain experimental window to another one where tests are not available is very risky...
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谢谢你，Ben。我大体上同意你的观点，尽管我对2D DIC的使用有点怀疑。当然，在响应的弹性部分，不可避免的离面运动使得几乎不可能获得鲁棒弹性常数。已经发表了一些技巧来克服这个问题，但根据我的看法，需要额外的努力意味着立体dic仍然是最佳选择。就成本而言，额外的相机不是问题，校准所需的额外时间也不是决定因素。对于斑点，我认为我们应该远离喷漆，因为这不能保证高质量的测量，并且给不确定性量化增加了随机因素，例如会妨碍标准化。就像使用一个随机变化的应变计。
Your comment about the level of expertise is interesting, and I certainly agree to part of it. Let me explain. If we are talking about routine tests for, say, material characterisation, including quality control, we need standards that could be followed by lab technicians with a detailed operational roadmap. On the other hand, outside of this framework, the complexity of DIC is akin to that of finite element simulation and handling this without thorough training and understanding of the theory is a recipe for disaster. As for numerical simulation, DIC brings a lot of information but there is a price to pay in terms of training.
As for data inversion, there is scope for progress indeed, but the current tools (FE updating, VFM) are already sufficiently mature to establish new MT2.0test configurations now. Regardless of the technical nitty-gritty, I think a vast space of improvement is in the very formulation of constitutive material models. For anisotropic plasticity for instance, models for yield surfaces are closed, i.e., the model predicts the behaviour for any combination of stress components. This is because the identification based on statically determinate tests only puts one point in the stress space for each test. With MT2.0, we have a point cloud, so it would be possible to formulate a yield surface using B-spline interpolation for instance, which would provide very robust data in the space activated by the test. But this would not provide any information elsewhere. This may be seen as a weakness but I think it is a strength as if you want data in a particular part of the stress space, you should perform a test providing data there. Uniaxial tests do not tell you anything about a biaxial stress state. Extrapolation from a certain experimental window to another one where tests are not available is very risky...
精彩概述!
< a>< p>< a>< p>< div class="field- name-comment-body field-type-text-long field-label-hidden">一些想法:
关于材料测试2.0的成本，与之前的方法(如拉伸测试(材料测试1.0))相比，我相信当采用只需要2D DIC的方法时，可以实现类似的成本。要做到这一点，所需要的只是一个相机和一种应用DIC模式的方法(例如，喷漆)。当然，这并不适用于所有问题。
在评估方法时要考虑的一个关键因素是所需的用户专业知识水平。开发方法和软件，使非专业人员能够获得广泛问题的可靠结果，这一点至关重要。
不同的逆识别是一个开放的问题，对各种方法的持续研究可能会大大提高材料测试2.0的实用性。这包括利用机器学习和优化方面的现代进步，以及基于独特假设集的替代理论方法的发展，这些方法可以提供显着的优势(我目前正在研究这个领域的一些想法)。似乎可以解决的关键挑战包括实验信息的成本(立体DIC/二维DIC;更简单的实验测试)，覆盖范围更广的材料和变形制度，准确性，可靠性和用户的专业知识。