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Meeting MS&T21: Materials Science & Technology
Symposium Additive Manufacturing of Metals: ICME Gaps: Material Property and Validation Data to Support Certification
Presentation Title Predicting Melt Properties Using Atomistic Simulations with a Highly Accurate Physically Informed Neural Network Interatomic Potential
Author(s) Vesselin I. Yamakov, Yuri Mishin, Edward H Glaessgen
On-Site Speaker (Planned) Vesselin I. Yamakov
Abstract Scope This presentation discusses the use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties. Such properties are critical for process modelling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.


An Analysis of the Dislocation Density of Inconel 718 Additive Manufacturing Powder
An ICME Approach for Designing Appropriate Heat Treatments in Additively Manufactured Nitrogen Atomized 17-4PH Stainless Steel
Capturing and Analyzing In-situ Data within the Directed Energy Deposition Process with DEDSmart
CFD Modelling for AM Processes
Critical Issues and Gaps in Testing and Characterization Data for Computational Materials in Qualification and Certification of Additively Manufactured Metallic Materials
Determining Data Requirements to Quantify Porosity in the Laser Powder Bed Fusion Process
Enabling Quality Assurance by Completing the Process-Property-Performance Paradigm for Additive Manufacturing
Experimental and Numerical Investigation of Pressureless Sintering for Binder Jetted Metal Parts
High Temperature Material Properties Measurement Capabilities of the NASA MSFC Electrostatic Levitation (ESL) Laboratory
High Temperature Material Property Data and Challenges to Thermal Process Model Predictions and In-Situ/Ex-Situ Measurements for Metallic Additive Manufacturing
ICME Gap Analysis for Materials Design and Process Optimization in Additive Manufacturing
ICME Gaps for Additive Manufacturing of Metals
Laser Energy Coupling during Metal Additive Manufacturing
Lessons Learned from Calibration and Validation of Process Models for Laser Powder Bed Fusion
Methods for Improved Part-scale Thermal Process Simulations in Laser Powder Bed Fusion
On Scan Path Knowledge for Model Informed Process Planning and Material Quality Predictions
Phase Field Informed Monte Carlo Texture Evolution Models for Additive Manufacturing Microstructure Simulation and the Need for Experimental Grain Competition Data
Predicting Melt Properties Using Atomistic Simulations with a Highly Accurate Physically Informed Neural Network Interatomic Potential
Providing a Rigorous Measurement Foundation for Modeling-Informed Qualification and Certification of Metal AM Components
Transferability of Terrestrial Development of Metal Additive to Extraterrestrial Applications

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