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

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Abstract

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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Research and Innovation Center, National Energy Technology Laboratory, Albany, OR, 97321, USA

    Madison Wenzlick, Jennifer R. Bauer, Kelly Rose & Jeffrey Hawk

  2. Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA

    Madison Wenzlick

  3. Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99352, USA

    Ram Devanathan

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  1. Madison Wenzlick
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  2. Jennifer R. Bauer
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  3. Kelly Rose
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  5. Ram Devanathan
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Correspondence to Ram Devanathan.

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Disclaimer This work was funded by the Department of Energy, National Energy Technology Laboratory, an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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Wenzlick, M., Bauer, J.R., Rose, K. et al. Data Assessment Method to Support the Development of Creep-Resistant Alloys. Integr Mater Manuf Innov 9, 89–102 (2020). https://doi.org/10.1007/s40192-020-00167-3

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