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首页> 美国卫生研究院文献>Materials >Cyclic Plasticity and Low Cycle Fatigue of an AISI 316L Stainless Steel: Experimental Evaluation of Material Parameters for Durability Design
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Cyclic Plasticity and Low Cycle Fatigue of an AISI 316L Stainless Steel: Experimental Evaluation of Material Parameters for Durability Design

机译：AISI 316L不锈钢的循环可塑性和低循环疲劳：耐用性设计材料参数的实验评价

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摘要

AISI 316L stainless steels are widely employed in applications where durability is crucial. For this reason, an accurate prediction of its behaviour is of paramount importance. In this work, the spotlight is on the cyclic response and low-cycle fatigue performance of this material, at room temperature. Particularly, the first aim of this work is to experimentally test this material and use the results as input to calibrate the parameters involved in a kinematic and isotropic nonlinear plasticity model (Chaboche and Voce). This procedure is conducted through a newly developed calibration procedure to minimise the parameter estimates errors. Experimental data are eventually used also to estimate the strain–life curve, namely the Manson–Coffin curve representing the 50% failure probability and, afterwards, the design strain–life curves (at 5% failure probability) obtained by four statistical methods (i.e., deterministic, “Equivalent Prediction Interval”, univariate tolerance interval, Owen’s tolerance interval for regression). Besides the characterisation of the AISI 316L stainless steel, the statistical methodology presented in this work appears to be an efficient tool for engineers dealing with durability problems as it allows one to select fatigue strength curves at various failure probabilities depending on the sought safety level.
机译：AISI 316L不锈钢广泛用于耐久性至关重要的应用中。因此，准确地预测其行为至关重要。在这项工作中，聚光灯处于室温下的循环响应和低循环疲劳性能。特别是，这项工作的首次目的是通过实验测试该材料并使用结果作为输入来校准运动学和各向同性非线性塑性模型（Chaboche和Voce）中涉及的参数。通过新开发的校准程序进行此过程以最小化参数估计误差。最终还使用实验数据来估计应变 - 寿命曲线，即代表50％失效概率的Manson-coffin曲线，之后，通过四种统计方法获得的设计应变寿命曲线（以5％的故障概率）（即，确定性，“等效预测间隔”，单变量公差间隔，欧文的回归的公差间隔）。除了AISI 316L不锈钢的表征外，本作品中提出的统计方法似乎是处理耐用性问题的工程师的有效工具，因为它允许其中选择各种故障概率的疲劳强度曲线，具体取决于所寻求的安全水平。

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