ProgramMaster Logo
Conference Tools for MS&T24: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T24: Materials Science & Technology
Symposium Computational Materials for Qualification and Certification
Presentation Title Uncertainty Quantification in Process-Structure-Property Dynamics of IN718
Author(s) Hasan Al Jame, S. Mohadeseh Taheri-Mousavi
On-Site Speaker (Planned) Hasan Al Jame
Abstract Scope Inconel 718 is a widely utilized superalloy in extreme-temperature applications for its high-temperature properties. While it is one of the few commercially printable alloys, the sensitivity and uncertainty within its microstructural features and properties are undetermined. Here, we have combined machine learning with CALPHAD-based ICME techniques to address these challenges. The superior properties of the alloy come from the stability of its strengthening phases in the microstructure, whereas some phases are detrimental to high-temperature applications. In this study, we focus on four main phases of this alloy in as-built and aged conditions and measure the sensitivity of elemental and compositional variation on phase fractions. We further extend our sensitivity analysis to the material’s properties such as strength and printability. Our goal is to discuss how these uncertainties in microstructural features will propagate to the uncertainty in these properties to delineate potential design pathways to achieve robust 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

Questions about ProgramMaster? Contact programming@programmaster.org