Abstract
Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM). This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts. Furthermore, data collection at the microscale is costly. Consequently, this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure. This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures. The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new, arbitrary microstructures can be predicted. The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.
Highlights
The process used to manufacture a material governs its morphological structure, which in turn drives the values and spatial distributions of the properties of the processed material and its performance
The set of inputs needed for a crystal plasticity finite element (CPFE) model are the uniform kinematic displacement boundary conditions (u, vector of functional variables) applied to the faces of an representative volume element (RVE) over the duration of the simulation, constitutive model parameters (θ, scalar variables) that define the material behavior, and the microstructure morphology
During each time increment of the CPFE simulation, a step in displacement is taken based on the value specified by the loading parameter, and along with the previous state of stress and strain in an element, a new element strain is computed followed by a stress update for the element in the current increment
Summary
The process used to manufacture a material governs its morphological structure, which in turn drives the values and spatial distributions of the properties of the processed material and its performance. Many works have attempted to understand PSP relational linkages in AM using high fidelity, multiscale simulations[20,21], which can achieve high accuracy predictions These simulations are typically computationally expensive, making them better suited for understanding the underlying physics rather than being used for rapid production and/or qualification[22]. DL requires a large training data set that can be infeasible to generate using the computationally CPFE model It is not clear whether this method will yield acceptable performance when applied to polycrystalline microstructures, especially those with the complexity of AM microstructures, as this has not been addressed in existing literature studies. The drawback of the fGP models developed in the previous works
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