TY - JOUR
T1 - Data-driven prediction of mechanical properties in support of rapid certification of additively manufactured alloys
AU - Yan, Fuyao
AU - Chan, Yu Chin
AU - Saboo, Abhinav
AU - Shah, Jiten
AU - Olson, Gregory B.
AU - Chen, Wei
N1 - Funding Information:
This work was supported by the Digital Manufacturing and Design Innovation Institute (DMDII) through award number 15-07-07. This material is also based upon the work of Ms. Yu-Chin Chan supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1842165. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
Copyright © 2018 Tech Science Press.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Additive manufacturing
KW - Gaussian process modeling
KW - Maraging stainless steel
KW - Spatially-varying properties
KW - Statistical sensitivity analysis
KW - Yield strength
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U2 - 10.31614/cmes.2018.04452
DO - 10.31614/cmes.2018.04452
M3 - Article
AN - SCOPUS:85059768370
SN - 1526-1492
VL - 117
SP - 343
EP - 366
JO - CMES - Computer Modeling in Engineering and Sciences
JF - CMES - Computer Modeling in Engineering and Sciences
IS - 3
ER -