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Pragmatic optimisation methods for determining material constants of viscoplasticity model from isothermal experimental data

机译:从等温实验数据确定粘塑性模型材料常数的实用优化方法

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摘要

A procedure to estimate material constants for the unified Chaboche viscoplasticity model from experimental data has been published elsewhere;1'2 however several critical assumptions are made to enable this and potential numerical problems can limit the effectiveness of the optimisation. Pragmatic optimisation procedures are therefore required to determine the material properties accurately and efficiently. This is made more complex by the presence of several deformation mechanisms and their interactions. Automation is critical due to the large amounts of data generated in testing. Complications that inhibit this process can arise due to factors such as experimental scatter. In this paper, a general optimisation framework is discussed and investigated using data from isothermal tests on a P91 steel at 600°C. Potential obstacles in the procedure are addressed and solutions (such as pre-optimisation experimental data 'cleaning') are suggested. Methods to maximise the amount of confidence a user has in a particular optimised constant set are also discussed.
机译:一种从实验数据估算统一的Chaboche粘塑性模型的材料常数的程序已经在其他地方发表; 1'2,但是做出了一些关键的假设来实现这一点,并且潜在的数值问题可能会限制优化的有效性。因此,需要务实的优化程序来准确有效地确定材料性能。由于存在几种变形机制及其相互作用,因此使情况变得更加复杂。由于测试中会生成大量数据,因此自动化至关重要。由于诸如实验分散等因素,可能会出现抑制该过程的并发症。在本文中,讨论了一个通用的优化框架,并使用了在600°C下对P91钢进行等温试验获得的数据进行了研究。解决了该程序中的潜在障碍,并提出了解决方案(例如预优化实验数据“清理”)。还讨论了最大化用户对特定优化常数集的置信度的方法。

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