Physics-Informed Network Models: a Data Science Approach to Metal Design

Integrating Materials and Manufacturing Innovation volume 6, pages279–287(2017)Cite this article

Abstract

Functional graded materials (FGM) allow for reconciliation of conflicting design constraints at different locations in the material. This optimization requires a priori knowledge of how different architectural measures are interdependent and combine to control material performance. In this work, an aluminum FGM was used as a model system to present a new network modeling approach that captures the relationship between design parameters and allows an easy interpretation. The approach, in an un-biased manner, successfully captured the expected relationships and was capable of predicting the hardness as a function of composition.

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Acknowledgments

The material for this study was provided by the Alcoa/Arconic Alloy Technology Division. This work was funded by the Army Research Office Short Term Innovative Research program: W911NF-14-0549. The data and R-analytics code utilized for the paper can be found at https://cwru-msl.github.io/MRL17/.

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Affiliations

  1. Department of Materials Science and Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Amit K. Verma, Roger H. French & Jennifer L. W. Carter

  2. SDLE Research Center, Case Western Research University, Cleveland, OH, 44106, USA

    Roger H. French

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  1. Amit K. Verma
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Correspondence to Jennifer L. W. Carter.

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Verma, A.K., French, R.H. & Carter, J.L.W. Physics-Informed Network Models: a Data Science Approach to Metal Design. Integr Mater Manuf Innov 6, 279–287 (2017). https://doi.org/10.1007/s40192-017-0104-5

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Keywords

  • Metal design
  • Network models
  • Optimization
  • Functional gradient materials