Study of Uniaxial Tensile Properties of Hexagonal Boron Nitride Nanoribbons

Uniaxial tensile properties of hexagonal boron nitride nanoribbons and dependence of these properties on temperature, strain rate, and the inclusion of vacancy defects have been explored with molecular dynamics simulations using Tersoff potential. The ultimate tensile strength of pristine hexagonal boron nitride nanoribbon of 26 nm x 5 nm with armchair chirality is found to be 100.5 GPa. The ultimate tensile strength and strain have been found decreasing with increasing the temperature while an opposite trend has been observed for increasing the strain rate. Furthermore, the vacancy defects reduce ultimate tensile strength and strain where the effect of bi-vacancy is clearly dominating over point vacancy

Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process

A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the integration of sensor data and user input. In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities. This paper introduces a parameterized physics-based digital twin (PPB-DT) for the statistical predictions of LPBF metal additive manufacturing process. We accomplish this by creating a high-fidelity computational model that accurately represents the melt pool phenomena and subsequently calibrating and validating it through controlled experiments. In PPB-DT, a mechanistic reduced-order method-driven stochastic calibration process is introduced, which enables the statistical predictions of the melt pool geometries and the identification of defects such as lack-of-fusion porosity and surface roughness, specifically for diagnostic applications. Leveraging data derived from this physics-based model and experiments, we have trained a machine learning-based digital twin (PPB-ML-DT) model for predicting, monitoring, and controlling melt pool geometries. These proposed digital twin models can be employed for predictions, control, optimization, and quality assurance within the LPBF process, ultimately expediting product development and certification in LPBF-based metal additive manufacturing.Comment: arXiv admin note: text overlap with arXiv:2208.0290

Accelerated and interpretable prediction of local properties in composites

The localized stress and strain field simulation results are critical for understanding the mechanical properties of materials, such as strength and toughness. However, applying off-the-shelf machine learning or deep learning methods to a digitized microstructure restricts the image samples to be of a fixed size and also lacks interpretability. Additionally, existing methods that utilize deep learning models to solve boundary value problems require retraining the model for each set of boundary conditions. To address these limitations, we propose a customized Pixel-Wise Convolutional Neural Network (PWCNN) to make fast predictions of stress and strain fields pixel-by-pixel under different loading conditions and for a wide range of composite microstructures of any size (e.g., much larger or smaller than the sample on which the PWCNN is trained). Through numerical experiments, we show that our PWCNN model serves as an alternative approach to numerical solution methods, such as finite element analysis, but is computationally more efficient, and the prediction errors on the test microstructure are around 5%. Moreover, we also propose an interpretable machine learning framework to facilitate the scientific discovery of why certain microstructures have better or worse performance than others, which has important implications in the design of composite microstructures in advanced manufacturing