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Calibration of thermal spray microstructure simulations using Bayesian optimization

Computational Materials Science

De Zapiain, David M.; Bays, Nathan R.; Moore, Nathan W.; Rodgers, Theron M.

Thermal spray deposition is an inherently stochastic manufacturing process used for generating thick coatings of metals, ceramics and composites. The generated coatings exhibit hierarchically complex internal structures that affect the overall properties of the coating. The deposition process can be adequately simulated using rules-based process simulations. Nevertheless, in order for the simulation to accurately model particle spreading upon deposition, a set of predefined rules and parameters need to be calibrated to the specific material and processing conditions of interest. The calibration process is not trivial given the fact that many parameters do not correspond directly to experimentally measurable quantities. This work presents a protocol that automatically calibrates the parameters and rules of a given simulation in order to generate the synthetic microstructures with the closest statistics to an experimentally generated coating. Specifically, this work developed a protocol for tantalum coatings prepared using air plasma spray. The protocol starts by quantifying the internal structure using 2-point statistics and then representing it in a low-dimensional space using Principal Component Analysis. Subsequently, our protocol leverages Bayesian optimization to determine the parameters that yield the minimum distance between synthetic microstructure and the experimental coating in the low-dimensional space.

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TYPE Journal Article YEAR 2024
DOIOSTIScopus

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

Journal of the Electrochemical Society

De Zapiain, David M.; Maestas, Demitri; Roop, Matthew; Noell, Philip J.; Melia, Michael A.; Katona, Ryan M.

Highlights Novel protocol for extracting knowledge from previously performed Finite Element corrosion simulations using machine learning. Obtain accurate predictions for corrosion current 5 orders of magnitude faster than Finite Element simulations. Accurate machine learning based model capable of performing an effective and efficient search over the multi-dimensional input space to identify areas/zones where corrosion is more (or less) noticeable.

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TYPE Journal Article YEAR 2024
DOIOSTIScopus

Neural network ensembles and uncertainty estimation for predictions of inelastic mechanical deformation using a finite element method-neural network approach

Data-Centric Engineering

Bergel, Guy L.; De Zapiain, David M.; Romero, Vicente J.

The finite element method (FEM) is widely used to simulate a variety of physics phenomena. Approaches that integrate FEM with neural networks (NNs) are typically leveraged as an alternative to conducting expensive FEM simulations in order to reduce the computational cost without significantly sacrificing accuracy. However, these methods can produce biased predictions that deviate from those obtained with FEM, since these hybrid FEM-NN approaches rely on approximations trained using physically relevant quantities. In this work, an uncertainty estimation framework is introduced that leverages ensembles of Bayesian neural networks to produce diverse sets of predictions using a hybrid FEM-NN approach that approximates internal forces on a deforming solid body. The uncertainty estimator developed herein reliably infers upper bounds of bias/variance in the predictions for a wide range of interpolation and extrapolation cases using a three-element FEM-NN model of a bar undergoing plastic deformation. This proposed framework offers a powerful tool for assessing the reliability of physics-based surrogate models by establishing uncertainty estimates for predictions spanning a wide range of possible load cases.

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TYPE Journal Article YEAR 2023
DOIOSTIScopus

Training data selection for accuracy and transferability of interatomic potentials

npj Computational Materials

De Zapiain, David M.; Wood, M.A.; Lubbers, Nicholas; Pereyra, Carlos Z.; Thompson, Aidan P.; Perez, Danny

Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus
Results 26–50 of 76
Results 26–50 of 76
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