Data-driven prediction of mechanical properties in support of rapid certification of additively manufactured alloys

Fuyao Yan, Yu Chin Chan, Abhinav Saboo, Jiten Shah, Gregory B. Olson, Wei Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Predicting the mechanical properties of additively manufactured parts is often a tedious process, requiring the integration of multiple stand-alone and expensive simulations. Furthermore, as properties are highly location-dependent due to repeated heating and cooling cycles, the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification, compounding the computational expense. This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels, a type of machine learning model for statistical inference with limited data. The metamodels can predict the varying properties within an entire part in a fraction of the time while providing uncertainty quantification. The framework was demonstrated with the prediction of the tensile yield strength of Ferrium ® PH48S maraging stainless steel fabricated by additive manufacturing. Impressive agreement was found between the metamodels and the mechanistic models, and the computation was dramatically decreased from hours of physics-based simulations to less than a second with metamodels. This method can be extended to predict various materials properties in different alloy systems whose process-structure-property-performance interrelationships are linked by mechanistic models. It is powerful for rapidly identifying the spatial properties of a part with compositional and processing parameter variations, and can support part certification by providing a fast interface between materials models and part-level thermal and performance simulations.

Original languageEnglish (US)
Pages (from-to)343-366
Number of pages24
JournalCMES - Computer Modeling in Engineering and Sciences
Volume117
Issue number3
DOIs
StatePublished - 2018

Keywords

  • Additive manufacturing
  • Gaussian process modeling
  • Maraging stainless steel
  • Spatially-varying properties
  • Statistical sensitivity analysis
  • Yield strength

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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