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Bayesian modeling of inconsistent plastic response due to material variability

机译：由于物质变异性导致的贝叶斯建模不一致

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摘要

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. We demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters. Published by Elsevier B.V.
机译：制造技术的出现，如添加剂制造，对由于缺陷和其他微观结构方面引起的材料响应相当可变性的关注。这种变异性激励了增强设计方法的发展，该方法包含固有的物质变化，以提供性能的强大预测。在这项工作中，我们开发了使用平均反应的传统塑性模型在实验中观察到的机械响应分布的可塑性模型，并且最近开发了不确定性量化（UQ）技术。要考虑通过物理参数的变化进行材料响应可变性，我们适应最近贝叶斯嵌入式误差校准技术。我们使用Bayesian模型选择来确定各种可塑性模型的最合理性和参数变异性的最佳嵌入。为了加快模型选择，我们开发了基于自适应的重要性采样的数值集成方案，以计算贝叶斯模型证据。我们展示新框架提供了更优于更传统的预测性的实现，以及这些UQ技术如何用于模型选择并评估校准物理参数的质量。由elsevier b.v出版。

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