Data Assessment Method to Support the Development of Creep-Resistant Alloys

  • Madison Wenzlick
  • Jennifer R. Bauer
  • Kelly Rose
  • Jeffrey Hawk
  • Ram DevanathanEmail author
Technical Article


This work introduces a methodology to assess data quality for the tensile, creep/stress relaxation, and fatigue properties of alloys (as well as metadata associated with manufacture) as a part of a project to develop new materials for extreme environments. The extreme environments in question deal with those found in the power generation sector. Data quality assessment is needed to ensure the reliability of data used in analytics to develop new materials for the power generation sector and to predict the performance of established materials in current use. As data quality metrics have not been standardized for material properties data, quality rating guidelines are developed here for the aspects of data completeness, accuracy, usability, and standardization. The specific design requirements for heat-resistant alloy development were considered in creating each metric. Establishing the quality of a dataset in these areas will enable robust analysis. High-quality data can be set aside to develop predictive models. Lower-quality data need not be discarded but can be used for experimental design. Determining the quality of a materials dataset will also provide additional metadata with the data resource and will promote data reusability. A sample high-quality dataset is presented to indicate the typical data attributes collected from relevant mechanical property testing results, which were considered when generating the data quality metrics. A data template of these attributes was created as a tool for data generators and collectors to promote uniformity and reusability of alloy data. The sparsity of the sample dataset was calculated in order to highlight the areas where data gaps pose a challenge for reliable prediction of creep rupture lifetime.


Data quality Alloy design Mechanical properties Data reusability 



This work was supported by the NETL Crosscutting Research Program, Briggs White, NETL Technology Manager, and Regis Conrad, DOE-FE HQ Program Manager. This work was executed through the eXtremeMAT National Laboratory Field Work Proposal (NETL: FWP-1022433, PNNL: FWP-71133). This project was supported in part by an appointment to the Science Education Programs at National Energy Technology Laboratory (NETL), administered by Oak Ridge Associated Universities through the U.S. Department of Energy Oak Ridge Institute for Science and Education. The authors thank John Oakey for stimulating discussions on data quality metrics based on his work in this area.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Supplementary material 1 (DOCX 147 kb)
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Supplementary material 2 (XLSX 18 kb)
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Supplementary material 3 (XLSX 27 kb)


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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection  2020

Authors and Affiliations

  1. 1.Research and Innovation CenterNational Energy Technology LaboratoryAlbanyUSA
  2. 2.Oak Ridge Institute for Science and EducationOak RidgeUSA
  3. 3.Energy and Environment DirectoratePacific Northwest National LaboratoryRichlandUSA

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