TY - EJOU AU - Yan, Fuyao AU - Chan, Yu hin AU - Saboo, Abhinav AU - Shah, Jiten AU - Olson, Gregory B. AU - Chen, Wei TI - Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys T2 - Computer Modeling in Engineering \& Sciences PY - 2018 VL - 117 IS - 3 SN - 1526-1506 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 - spatially-varying properties KW - Gaussian process modeling KW - statistical sensitivity analysis KW - maraging stainless steel KW - yield strength DO - 10.31614/cmes.2018.04452