@Article{cmes.2018.04452,
AUTHOR = {Fuyao Yan, Yu hin Chan, Abhinav Saboo, Jiten Shah, Gregory B. Olson, Wei Chen},
TITLE = {Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {117},
YEAR = {2018},
NUMBER = {3},
PAGES = {343--366},
URL = {http://www.techscience.com/CMES/v117n3/33819},
ISSN = {1526-1506},
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 <sup>®</sup> 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.},
DOI = {10.31614/cmes.2018.04452}
}