Automated Calculation of Strain Hardening Parameters from Tensile Stress vs. Strain Data for Low Carbon Steel Exhibiting Yield Point Elongation

IF 1.5 4区 工程技术 Q3 ENGINEERING, MECHANICAL Experimental Techniques Pub Date : 2023-02-09 DOI:10.1007/s40799-023-00626-4
M. Scales, J.A. Kornuta, N. Switzner, P. Veloo
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引用次数: 1

Abstract

Existing guidance from ASTM standards on extracting mechanical properties from uniaxial tension test data is not suitable for high-volume applications because it lacks automation. The Pacific Gas and Electric Company (PG&E) has performed over 450 uniaxial tension tests on samples extracted from dozens of distinct natural gas pipeline features in support of materials verification efforts; 144 of these tests recorded the entire deformation response up to fracture and were subsequently analyzed herein. Algorithms were developed to enable automatic, batch post-processing of the tensile data. Among the mechanical properties that this software extracts from the tensile data are the power-law hardening exponent and strength coefficient. A novel algorithm was developed to calculate these power-law parameters while accommodating the range of yielding and hardening behaviors present among the low carbon pipeline steels tested in this effort. The algorithm, presented in this brief technical note, first identifies the lower limit of the hardening region by calculating the tangent modulus of the stress–strain curve, which reaches its maximum value at the onset of strain hardening. The end of uniaxial hardening coincides with the ultimate tensile stress and the end of uniform deformation. From there the algorithm computes the power-law hardening parameters using conventional linear regression. Analysis of the data shows that this approach is more accurate than two other approaches: (1) regressing from 0.2% plastic strain to the limit load, and (2) iteratively regressing to identify the region that minimizes the regression error. The advantage of this approach, over existing methods, is observed for materials that exhibit a yield plateau. To further demonstrate the utility of the strain hardening information collected during this effort, the relationship between strain hardening exponent and ratio of yield strength to ultimate tensile strength from this data was compared to those reported in previous literature pertaining to remaining life assessments of steel line pipe. Overall, the strain hardening data obtained in this algorithm indicates greater hardening in pipeline steels than some previously published results. This algorithm has been made available as part of this paper’s supplementary materials.

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从显示屈服点伸长率的低碳钢的拉应力与应变数据中自动计算应变硬化参数
ASTM标准中关于从单轴拉伸试验数据中提取机械性能的现有指南不适合大批量应用,因为它缺乏自动化。太平洋天然气和电力公司(PG&E)对从数十种不同的天然气管道特征中提取的样品进行了450多次单轴拉伸测试,以支持材料验证工作;其中144次试验记录了断裂前的整个变形响应,并在此进行了分析。算法的发展,使自动,批量后处理的拉伸数据。该软件从拉伸数据中提取的力学性能包括幂律硬化指数和强度系数。研究人员开发了一种新的算法来计算这些幂律参数,同时适应低碳管线钢的屈服和硬化行为范围。本文介绍的算法首先通过计算应力-应变曲线的切模量来确定硬化区域的下限,切模量在应变硬化开始时达到最大值。单轴硬化的结束与拉伸应力的极限和均匀变形的结束重合。在此基础上,算法利用常规线性回归计算幂律强化参数。数据分析表明,该方法比其他两种方法(1)从0.2%塑性应变回归到极限载荷,(2)迭代回归识别回归误差最小的区域更准确。这种方法的优势,超过现有的方法,是观察到的材料表现出一个产量平台。为了进一步证明在此过程中收集的应变硬化信息的效用,将应变硬化指数与屈服强度与极限抗拉强度之比之间的关系与先前有关钢管剩余寿命评估的文献进行了比较。总体而言,该算法获得的应变硬化数据表明,管道钢的硬化程度高于先前发表的一些结果。该算法已作为本文补充材料的一部分提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Experimental Techniques
Experimental Techniques 工程技术-材料科学:表征与测试
CiteScore
3.50
自引率
6.20%
发文量
88
审稿时长
5.2 months
期刊介绍: Experimental Techniques is a bimonthly interdisciplinary publication of the Society for Experimental Mechanics focusing on the development, application and tutorial of experimental mechanics techniques. The purpose for Experimental Techniques is to promote pedagogical, technical and practical advancements in experimental mechanics while supporting the Society''s mission and commitment to interdisciplinary application, research and development, education, and active promotion of experimental methods to: - Increase the knowledge of physical phenomena - Further the understanding of the behavior of materials, structures, and systems - Provide the necessary physical observations necessary to improve and assess new analytical and computational approaches.
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