Open Access

ARTICLE

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

Fuyao Yan^{1, #}, Yu hin Chan^2,#, Abhinav Saboo³ , Jiten Shah⁴, Gregory B. Olson^{1, 3}, Wei Chen^{2, *}

1 Department of Materials Science and Engineering, Northwestern University, 2220 Campus Dr, Evanston, IL 60208, USA.
2 Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA.
3 QuesTek Innovations LLC, 1820 Ridge Ave, Evanston, IL 60201, USA.
4 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.

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Keywords

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