...
首页> 外文期刊>Journal of Engineering for Gas Turbines and Power >Improvement of the Inverse Finite Element Analysis Approach for Tensile and Toughness Predictions by Means of Small Punch Technique
【24h】

Improvement of the Inverse Finite Element Analysis Approach for Tensile and Toughness Predictions by Means of Small Punch Technique

机译:通过小冲床技术改进抗拉技预测的逆有限元分析方法

获取原文
获取原文并翻译 | 示例

摘要

This paper focuses on the inverse finite element analysis (FEA) to calculate the small punch technique (SPT) tests and the prediction of the tensile and fracture toughness behavior. For the description of the SPT tests via FEA, the hardening rule of Ramberg-Osgood (RO) and the damage model of Gursson-Tvergaard-Needleman (GTN) were used. The inverse FEA optimization process cannot provide a unique solution for the 12 parameters included in the material model. This results from a dependency between some parameters, which leads to the same solution in the optimization. Hence, a novel description of the dependent parameters was developed and implemented within the optimization process. Therefore, an enhanced inverse FEA approach was proposed, which provides a fast converging solution for determination of the material model parameters. Within this study, the forged turbine shaft material EN: 27NiCrMoV15-6 was investigated. For comparison purpose, SPT tests as well as tensile tests and fracture toughness tests were carried out. In the case of the tensile properties, the test and simulation show coincidence in the curve and the characteristic values. For the toughness behavior, the characteristic value of the test was met by the simulation.
机译:本文侧重于逆有限元分析(FEA)来计算小冲孔技术(SPT)测试和拉伸和断裂韧性行为的预测。对于通过FEA的SPT测试的描述,使用了Ramberg-Osgood(RO)的硬化规则和Gbursson-Tvergaard-Candleman(GTN)的损伤模型。反向FEA优化过程不能为材料模型中包含的12个参数提供唯一的解决方案。这导致某些参数之间的依赖性,这导致了在优化中相同的解决方案。因此,在优化过程中开发并实施了对所属参数的新描述。因此,提出了增强的逆FEA方法,其提供了一种快速收敛溶液,用于确定材料模型参数。在本研究中,研究了锻造涡轮轴材料EN:27nicrmov15-6。为了比较目的,进行SPT测试以及拉伸试验和断裂韧性试验。在拉伸性能的情况下,测试和仿真在曲线和特征值中显示出巧合。对于韧性行为,通过模拟满足了测试的特征值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号