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Meeting MS&T24: Materials Science & Technology
Symposium Computational Materials for Qualification and Certification
Presentation Title Quantifying Microstructure Evolution of LPBF Ni-Alloy Under High Temperatures Exposure Through Computer Vision
Author(s) Yu-Tsen Yi, Junwon Seo, Nicholas Lamprinakos, Anthony Rollett
On-Site Speaker (Planned) Yu-Tsen Yi
Abstract Scope Haynes 282 (H282) is a nickel-based superalloy renowned for its superior creep resistance, rendering it exceptionally suitable for high-temperature applications. However, the microstructural and material property evolution at temperatures surpassing the recommended threshold of approximately 800°C has not been thoroughly explored. This study investigates the microstructural behavior of H282 fabricated using Laser Powder Bed Fusion (LPBF) under severe thermal conditions, reaching up to 1000°C, tailored for methane self-oxidation applications. A grain segmentation pipeline utilizing computer vision techniques was developed to expedite the analysis of grain morphology evolution. This pipeline rapidly segments and quantifies grain size and shape distribution from Scanning Electron Microscope (SEM) images, and the robustness of this method is validated through comparisons with Electron Backscatter Diffraction (EBSD) maps. This study will also encompass the size distribution of secondary phase precipitates, given their significant impact on creep properties.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Computational Multiscale Approach for Predicting Macroscale Elastic Properties and Failure Initiation in Phenolic Impregnated Carbon Ablator
A Framework for Assessing Simulation Maturity
Additive Manufacturing Porosity Estimation Using Multiple Nondestructive Evaluation Techniques
America Makes Efforts in Advanced Qualification Methods for AM
Assessing the Impact of Melt Pool Geometry Variability on Lack-of-Fusion Porosity and Fatigue Life in Powder Bed Fusion - Laser Beam Ti-6Al-4V
Computational Framework for Spatially-Dependent Melt Pool and Microstructure Simulations of Additively Manufactured Material
Computational Investigation on the Combined Effect of Pore Attributes on Strain Concentrators in Metal Additively Manufactured Materials
Computational Materials for Qualification and Certification Steering Group and Community Vision Roadmap
Computational Tools for Advancing Materials Maturity in Additive Manufacturing
Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost
Correlations of Additive Manufacturing Model-Based Process Metrics With Spatter-Induced Porosity in the Powder Bed Fusion-Laser Beam/Metallic Process
Data-Driven Process Uncertainty Analysis of Stochastic Lack-of-Fusion in Laser Powder Bed Fusion
Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance
Durability and Damage Tolerance of Powder-Bed Fusion Ti-6Al-4V: Current Results and Modeling Needs
Efficient Sensitivity and Uncertainty Analysis of a Laser Powder Bed Fusion Thermal Model Built Using HYPAD-FEM
Enabling Rapid Aerospace Component Qualification and Certification: Integrated Model-Based Material Definitions in Additive Manufacturing
Fast, Cheap & In Control: Application of Surrogate Models to Explore Microstructure-Properties Relationships for AM-Based Materials
GO-MELT: GPU-Optimized Multilevel Execution of LPBF Thermal Simulations
Industry's Vision for the Use of Computational Materials Tools in Qualification and Certification
Lessons Learned Calibration and Validation of Process Models for Laser Powder Bed Fusion Additive Manufacturing
Machine Learning Enabled Parametrically Upscaled Constitutive Models for Fatigue Simulations: A Data-Driven Multiscale Modeling Approach
Materials Data for Validation and Verification of Mechanical Performance: Outcomes and Future Perspectives from the AM Benchmark Series
Physics-Based Modeling of Ti-6Al-4V Phase Transformations for PBF-LB Temperature Histories
Process sensitivity of Laser Powder Bed Fusion of IN718 to Composition Variation
Quantification of Microstructure-Induced Uncertainty in Fatigue Nucleation in Polycrystalline Materials
Quantifying Microstructure Evolution of LPBF Ni-Alloy Under High Temperatures Exposure Through Computer Vision
QUASAR – Assessment of the State of the Art and Gaps for AM of Fracture Critical Components
Review of Past and Future Impacts of the Additive Manufacturing Benchmark Test Series (AM Bench)
Scientific AI for Automated Validation and Certification
Towards a Digital Twin for Qualification and Certification of Metals Additive Manufacturing
Towards a Probabilitic Model for the Assessment of Gas Turbine Components
Transitioning from Basic Research to Industrial Applications for Metal AM Components
Uncertainty Quantification and Sensitivity Analysis in Process-Structure-Property Simulations for Laser Powder Bed Fusion Additive Manufacturing
Uncertainty Quantification in Process-Structure-Property Dynamics of IN718
Using Unsupervised Learning to Cluster Fatigue Life Based on Ti64 Fatigue Fracture Surface Characteristics

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