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

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 processstructure-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.

Robust topology optimization of multi-material lattice structures under material and load uncertainties

Enabled by advancements in multi-material additive manufacturing,lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions.This work proposes a density-based robust topology optimization method for meso-or macroscale multi-material lattice structures under any combination of material and load uncertainties.The method utilizes a new generalized material interpolation scheme for an arbitrary number of materials,and employs univariate dimension reduction and Gauss-type quadrature to quantify and propagate uncertainty.By formulating the objective function as a weighted sum of the mean and standard deviation of compliance,the tradeoff between optimality and robustness can be studied and controlled.Examples of a cantilever beam lattice structure under various material and load uncertainty cases exhibit the efficiency and flexibility of the approach.The accuracy of univariate dimension reduction is validated by comparing the results to the Monte Carlo approach.