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Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys

Fuyao Yan1, #, Yu hin Chan2,#, Abhinav Saboo3 , Jiten Shah4, Gregory B. Olson1, 3, Wei Chen2, *
Department of Materials Science and Engineering, Northwestern University, 2220 Campus Dr, Evanston, IL 60208, USA.
Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA.
QuesTek Innovations LLC, 1820 Ridge Ave, Evanston, IL 60201, USA.
Product Development & Analysis LLC, 1776 Legacy Circle, Suite #115, Naperville, IL 60563, USA.
* Corresponding Author:Wei Chen. Email:
(This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)

Computer Modeling in Engineering & Sciences 2018, 117(3), 343-366. https://doi.org/10.31614/cmes.2018.04452

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.

Keywords

Additive manufacturing, spatially-varying properties, Gaussian process modeling, statistical sensitivity analysis, maraging stainless steel, yield strength.

Cite This Article

Yan, F., Chan, Y. H., Saboo, A., Shah, J., Olson, G. B. et al. (2018). Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys. CMES-Computer Modeling in Engineering & Sciences, 117(3), 343–366.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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