Accurate Estimation of Yield Strength and Ultimate Tensile Strength through Instrumented Indentation Testing and Chemical Composition Testing

Author:

Scales Martin,Anderson Joel,Kornuta Jeffrey A.,Switzner Nathan,Gonzalez Ramon,Veloo Peter

Abstract

Federal rule changes governing natural gas pipelines have made non-destructive techniques, such as instrumented indentation testing (IIT), an attractive alternative to destructive tests for verifying properties of steel pipeline segments that lack traceable records. Ongoing work from Pacific Gas and Electric Company’s (PG&E) materials verification program indicates that IIT measurements may be enhanced by incorporating chemical composition data. This paper presents data from PG&E’s large-scale IIT program that demonstrates the predictive capabilities of IIT and chemical composition data, with particular emphasis given to differences between ultimate tensile strength (UTS) and yield strength (YS). For this study, over 80 segments of line pipe were evaluated through tensile testing, IIT, and compositional testing by optical emission spectroscopy (OES) and laboratory combustion. IIT measurements of UTS were, generally, in better agreement with destructive tensile data than YS and exhibited about half as much variability as YS measurements on the same sample. The root-mean squared error for IIT measurements of UTS and YS, respectively, were 27 MPa (3.9 ksi) and 43 MPa (6.2 ksi). Next, a machine learning model was trained to estimate YS and UTS by combining IIT with chemical composition data. The agreement between the model’s estimated UTS and tensile UTS values was only slightly better than the IIT-only measurements, with an RMSE of 21 MPa (3.1 ksi). However, the YS estimates showed much greater improvement with an improved RMSE of 27 MPa (3.9 ksi). The experimental, mechanical, and metallurgical factors that contributed to IIT’s ability to consistently determine destructive UTS, and the differences in its interaction with composition as compared to YS, are discussed herein.

Publisher

MDPI AG

Subject

General Materials Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3