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首页> 外文会议>ASME(American Society of Mechanical Engineers) Pressure Vessels and Piping Conference vol.2: Computer Technology; 20050717-21; Denver,CO(US) >AUTOMATED PARAMETER DETERMINATION OF ADVANCED CONSTITUTIVE MODELS
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AUTOMATED PARAMETER DETERMINATION OF ADVANCED CONSTITUTIVE MODELS

机译：先进的本构模型的自动参数确定

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摘要

Parameter determination of advanced cyclic plasticity models which are developed for simulation of cyclic stress-strain and ratcheting responses is complex. This is mainly because of the large number of model parameters which are interdependent and three or more experimental responses are used in parameter determination. Hence the manual trial and error approach becomes quite tedious and time consuming for determining a reasonable set of parameters. Moreover, manual parameter determination for an advanced plasticity model requires in-depth knowledge of the model and experience with its parameter determination. These are few of the primary reasons for advanced cyclic plasticity models not being widely used for analysis and design of fatigue critical structures. These problems could be overcome through developing an automated parameter optimization system using heuristic search technique (e.g. genetic algorithm). This paper discusses the development of such an automatic parameter determination scheme for improved Chaboche model developed by Bari and Hassan. A new stepped GA optimization approach which is found to be more efficient over the conventional GA approach in terms of fitness quality and optimization time is presented.
机译：开发用于模拟循环应力应变和棘轮响应的高级循环可塑性模型的参数确定很复杂。这主要是由于大量相互依赖的模型参数，并且在参数确定中使用了三个或更多实验响应。因此，手动试错法对于确定合理的参数集变得非常繁琐且耗时。此外，用于高级可塑性模型的手动参数确定需要对模型有深入的了解，并需要有关其参数确定的经验。这些都是高级循环可塑性模型未广泛用于疲劳关键结构的分析和设计的几个主要原因。这些问题可以通过使用启发式搜索技术（例如遗传算法）开发自动参数优化系统来克服。本文讨论了由Bari和Hassan开发的，用于改进的Chaboche模型的这种自动参数确定方案的开发。提出了一种新的步进式GA优化方法，该方法在适应性质量和优化时间方面比常规GA方法更有效。

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