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Meeting MS&T24: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Presentation Title MALAMUTE Directed Energy Deposition Process Modeling and Experimental Validation through Investigation of Laser and Powder Efficiency
Author(s) Luis Nuņez, Wen Jiang, Dewen Yushu, Isabella van Rooyen, Michael Maughan
On-Site Speaker (Planned) Luis Nuņez
Abstract Scope Directed energy deposition (DED) process modeling can assist in understanding the physical phenomena related to the process fluctuations seen during DED fabrication. This study focuses on development and validation of a DED melt pool model using finite element method (FEM) using the Idaho National Laboratory Multiphysics Object Oriented Simulation Environment (MOOSE) framework-based Application Library for Advanced Manufacturing UTililiEs (MALAMUTE). Geometric measurements from parametric experimental study of laser power and powder feed rate of single-track clads fabricated on an Optomec LENS machine with 316L stainless steel and Inconel 718 are used to validate a 2D cross-sectional model. The developed MALAMUTE model is calibrated by investigating fluctuation in heat input with laser efficiency and mass input with powder efficiency to match the experimental measurements. Through a stochastic sampling of input parameters and efficiencies, machine learning surrogate models are trained to investigate and predict associated laser and powder efficiencies for reduced geometric error.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A-1: Development of A Customized Open-Source Inkjet 3D Printer
A Computational Approach to Optimize Phase Behavior in Compositionally Graded Structures
A Thermo-Mechanical Finite Element Model to Predict Thermal Cycles and Residual Stresses in Directed Energy Deposition Technology
Accelerated Post-Heat Treatment Design for Additive Manufacturing: A Case Study on Medium-High Entropy Alloy Using Commercial Alloy Powder Mixture
Accelerating Materials Development via ICME Automation: A Laser Beam Powder Bed Fusion Case Study
An Efficient Machine Learning Enhanced Image-Based Framework for Micromechanical Analysis of Additively Manufactured Ti-6Al-4V
Assessment of New Hot Crack Susceptibility Index Based on Interdendritic Pressure Drop Simulations
Bioinspired Fabrication and Mechanical Characterization of Concentric Cylindrical Structures: Integrating SLA Technology and Finite Element Analysis
CAD to Part Methodology for Process Structure and Performance (PSPP)
CALPHAD-Based ICME Design for Joining Dissimilar Alloys: Which Thermodynamic Database to Choose?
Combining Multi-Physics Simulations with Machine Learning to Elucidate Spatter Mechanisms and Establish Process Map in Laser Powder Bed Fusion
Computational and Experimental Phase Validation of Thermal Spray and Laser-Clad High-Entropy Alloy Coatings
Efficient Microstructure Prediction in Additive Manufacturing Using a Novel Dimension Reduction Method
Generative Property Optimization of Stochastic Microstructures
Heat Treatment Design for Laser-Melted Medium Entropy Alloys via Machine Learning and Gradient-Temperature Experiments
Integrating Phase Field Modeling and Machine Learning to Develop Process-Microstructure Relationships in Laser Powder Bed Fusion of IN718
Machine Learning Informed Inverse Design of an Additively Manufacturable Al Alloy Strengthened by Both Eutectic and Nanoprecipitates
Machine Learning Surrogate Model of Spatter Transport in a Laser Powder Bed Fusion Machine
MALAMUTE Directed Energy Deposition Process Modeling and Experimental Validation through Investigation of Laser and Powder Efficiency
Micro Cold Spray of Partially Sintered Zinc Oxide Nanoparticle Agglomerates
Microstructure-Sensitive Fatigue Models from Micromechanical Fatigue Experiments
Minimizing Layer-Level Thermal Variance in Electron Beam Powder Bed Fusion via Numerical Optimal Control
Modeling of Additively Manufactured Large-Components for Optimizing Powder Metallurgy Hot Isostatic Pressing Applications
Modeling of Shape Transition from Conduction to Keyholing for AA6061 in the Laser Power Bed Fusion Additive Manufacturing
Modeling of the Impact of Defects on Mechanical Properties of 3D Printed Natural Carbon-Enhanced Polymer Composites
On the Onset of Plasticity
Physics-Based Modeling for Process Dynamics and Microstructure Evolution in Laser Powder Bed Fusion
Process-Structure-Property Modeling for Fatigue in Additive Manufacturing
Process Design for Metal Additive Manufacturing Through High-Speed Imaging and Vision Transformers
Residual Stress in LHW-DED Ti-6Al-4V Single Walls
Sample Size Effect of Flaws on Fracture Behavior of Ti-6Al-4V by Laser Powder Bed Fusion: Experiments and Modeling
Self-Supervised Feature Distillation and Design of Experiments for Efficient Training of Micromechanical Deep Learning Surrogates
Simulation and Validation of Laser Powder Bed Fusion Melt Pool Physics through Multiphase Modeling
Study on Thermal Cracks in Steel Slab Using Neural Networks Model to Predict Impact Absorption Energy
Tailoring Distortion and Residual Stresses Using Hybrid Additive and Subtractive Approach
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