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 language | English (US) |
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Pages (from-to) | 343-366 |
Number of pages | 24 |
Journal | CMES - Computer Modeling in Engineering and Sciences |
Volume | 117 |
Issue number | 3 |
DOIs | |
State | Published - 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