Conference Logo ProgramMaster Logo
Conference Tools for MS&T26: 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&T26: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Presentation Title Validating Thermo-Calc Predictions of Oxygen Effects on LPBF Printability and Melt Pool Geometry in 316L Stainless Steel
Author(s) Ana L. Hernandez, Jhoan Guzman, Antonio J. Antonio
On-Site Speaker (Planned) Ana L. Hernandez
Abstract Scope LPBF requires computational tools capable of accelerating process optimization and understanding microstructure evolution under rapid solidification conditions. The Thermo-Calc AM Module was evaluated using experimental LPBF builds of 316L SS to assess its capability to predict printability maps, melt pool geometry, and defect formation. The effects of laser power, scanning speed, hatch spacing, and interstitial content were systematically investigated. Simulations revealed that increasing oxygen content shifts the lack-of-fusion boundary, expanding the printable region toward higher scanning speeds due to improved laser energy absorption and melt pool stability near the minimum penetration threshold. Melt pool depth increased with linear energy density, confirming its stronger physical relevance compared to volumetric energy density. However, oxygen content significantly affected melt pool behavior, indicating that compositional variations influence laser–material interaction. These results highlight the importance of considering oxygen effects in LPBF process modeling and demonstrate the usefulness of computational approaches for process window optimization.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Computational Toolkit for Microstructure-Property Mapping of Multiphase Complex Structured Materials
A Low Cost Geometrically Driven Laser Powder Bed Fusion Microstructure Model
A Material-Agnostic Framework for Rapid Generation of Updatable Process Maps for Powder-Blown Laser Directed Energy Deposition (L-DED)
A Multiscale Finite Element Analysis of the Dynamic Fragmentation of Additively Manufactured Porous Metal Rings
A Neural Network Approach for the Automated Classification of Material Textures
A Predictive Tool for Optimizing Processing Parameters Used in LPBF for 316L Stainless Steel
Accelerating High-Fidelity 3D Melt Pool Simulation with Latent Diffusion
AI-Enabled Modeling and Machine Learning for Process–Microstructure–Property Control in Additive Manufacturing
An Agentic AI-Assisted Workflow for Simulation-Informed WAAM of Nuclear Sensor Brackets
Beyond Single-Track Maps: Multi-Dimensional Process Mapping for LPBF Thermal Behavior and Microstructural Control
CNN Encoder–Decoder Segmentation of Fatigue-Relevant Surface Porosity in LPBF Ti-6Al-4V
Combined Effects of Pore Diameter, Orientation, and Stress State on Fracture of LB-PBF SS316L
Comparative Assessment of Crack Susceptibility Criteria Using CALPHAD Solidification Modeling and Graded Thermal Experiments
Comparison of Inert Gas Flow, Spatter Transport and Powder Pickup in Commercial and Open Format Laser Powder Bed Fusion Platforms
Constructing Surrogate Models with Constraints for Additive Manufacturing
Crack Growth and Failure Under High Cycle Fatigue of Pore-Containing 316L Stainless Steel Fabricated with Laser Powder Bed Fusion
Decoupling Energy Density Effects in Laser Powder Bed Fusion of Aluminum Alloy
Deterministic Microstructure Programming for Consistent and Functionally Graded Properties in Laser Powder Bed Fusion of Inconel 718
Efficient Melt-Pool Modeling Informed by Computational Fluid Dynamics for Part Scale Porosity and Microstructure Prediction
Experimental Investigation Into the Effects of Laser Parameters and Cooling Rates on Solidification Microstructures in Metal Additive Manufacturing
Fast Prediction of Thermal History in Powder Bed Fusion Through Autoregressive Transformer-Based Diffusion
Identifying Mechanical Drivers of Fatigue Damage in Additively Manufactured Inconel 718 Using In-Situ Synchrotron Characterization
Improving Lifing Predictions for Additively Manufactured Components Using the One-Part-And-Life (OPAL) Framework
In-Situ Targeted Reheating for Residual Stress Mitigation in Laser Directed Energy Deposition
Investigation of Near-Pore Microstructure in Laser Powder Bed Fusion Alloy 718
Large-Area EBSD Analysis of Laser Hot-Wire DED Additive Manufacturing
Large Scale Open-Source Experimental Data for Advancing Modeling and Analytics in Cold Spray Processing
Machine Learning-Based Defect Characterization from Fracture Surfaces in LPBF Ti-6Al-4V
Machine Learning-Based Processing Parameter Optimization of Additive Manufacturing of Soft Magnetic Steel
Microstructure-Aware Generative AI Model for Long-Term Spatiotemporally Consistent Prediction of Corrosion and Crack Evolution
Prediction of Defects, Microstructure, and Properties in Laser Powder Bed Fusion Using Physics-Aware Deep Learning
Process Parameter Optimization in Laser Powder Bed Fusion Additive Manufacturing
Property Optimization Through Full-Part Thermal History Control in Laser Powder Bed Fusion Additive Manufacturing
Quantitative Validation Methodologies for Physics-Based Microstructure Prediction Models in Metal Additive Manufacturing
Sensitivity of Grain-Averaged Elastic Strain and Orientation Predictions on the Mesh Density and Boundary Conditions in Crystal Plasticity Finite Element Simulations
Statistics-Based Modeling of Spatter-Related Defects Using High-Throughput CT and Operando Mechanistic Insights
Three-Dimensional Characterization and Modeling of Laser Powder Bed Fusion of 316L
Toward Transferable Dimensionless Process Maps for Active Learning-Based Optimization in Wire-Arc Additive Manufacturing
Validating Thermo-Calc Predictions of Oxygen Effects on LPBF Printability and Melt Pool Geometry in 316L Stainless Steel
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement