High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models

Abstract

Several challenges are encountered in the development of maraging steels with desired combinations of mechanical properties. These include the need to explore a large process design space, the time- and effort-consuming standardized testing protocols for property evaluations, and the lack of a formal design of experiments strategy that guides the selection of the process conditions for the next set of experiments based on a thorough analyses of the previously accumulated data. In this work, we explored the effect of the different combinations of the aging temperature and the aging time on the yield strength of 350-grade maraging steels using high-throughput protocols. For this purpose, a total of 21 small volumes of differently aged samples were produced and studied using spherical nanoindentation stress–strain protocols. Furthermore, the results of these tests were modeled using Gaussian process regression (GPR) to establish data-driven linkages between the yield strengths and the aging parameters. The predicted yield strengths were found to be in reasonable agreement with the experimentally measured ones. It is also demonstrated that the GPR model can provide objective guidance in the selection of the next set of experiments.

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Acknowledgements

The authors gratefully acknowledge support from N00014-18-1-2879. We also would like to thank the IEN/IMAT Materials Characterization Facility at Georgia Institute of Technology.

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Correspondence to Surya R. Kalidindi.

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Parvinian, S., Yabansu, Y.C., Khosravani, A. et al. High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models. Integr Mater Manuf Innov (2020). https://doi.org/10.1007/s40192-020-00177-1

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Keywords

  • Martensite
  • Maraging steels
  • Spherical nanoindentation
  • High throughput
  • Yield strength
  • Gaussian process regression
  • Uncertainty quantification
  • Process–property linkages