Additive Manufacturing Modeling, Simulation and Machine Learning Symposium

Additive Manufacturing Modeling, Simulation and Machine Learning Symposium is sponsored by The Minerals, Metals & Materials Society (TMS). This opportunity supports mission-aligned projects and measurable outcomes.

Additive Manufacturing Modeling, Simulation and Machine Learning Conference Tools for 2025 TMS Annual Meeting & Exhibition 2025 TMS Annual Meeting & Exhibition Additive Manufacturing Modeling, Simulation and Machine Learning TMS Materials Processing and Manufacturing Division TMS: Additive Manufacturing Committee TMS: Integrated Computational Materials Engineering Committee Jing Zhang , Purdue University Li Ma , Johns Hopkins Applied Physics Laboratory Brandon A.

McWilliams , US Army Research Laboratory Yeon-Gil Jung, Changwon National University This symposium will provide an excellent platform to exchange the latest knowledge in additive manufacturing (AM) modeling, simulation, artificial intelligence and machine learning. Despite extensive progress in AM field, there are still many challenges in predictive theoretical and computational approaches that hinder the advance of AM technologies.

The symposium is interested in receiving contributions in the following non-exclusive areas: In particular, the following topics, but not limited to, are of interest: 1. AM process modeling, monitoring and defect detection 2. Modeling of microstructure evolution, phase transformation, and defect formation in AM parts 3.

AM materials development using the Integrated computational materials engineering (ICME) approach 4. Modeling of residual stress, distortion, plasticity/damage, creep, and fatigue in AM parts 5. Modeling behaviors of AM materials in various environments (e.g., corrosion, high temperature, etc.) 6.

Computational modeling of process-structure-property-performance relationships for qualification of additive manufacturing 7. Artificial intelligence (AI), machine learning (ML) and data science applications to AM 8. Calibration and validation data sets relevant to models, uncertainty quantification 9.

Efficient computational methods using reduced-order models or fast emulators for process control 10.

Multiscale/multiphysics modeling strategies, including any or all of the scales associated with the spatial, temporal, and/or material domains Accelerating Crystal Plasticity Fatigue Simulations of Additively Manufactured Metals Using the “Materialize” Framework Additive Manufacturing Digital Twin (AMDT): Part Level Process Map Characterization Using Physics Based Simulation and Machine Learning Additive Manufacturing Process Modeling with Multi-Output Gaussian Processes Additive Manufacturing User Interface (AMUI): An Intuitive Software Suite for Part Level Process Parameter Selection AiDED: Accurate Machine Learning Inference Framework for Process Parameter Optimization in Laser Directed Energy Deposition AlloyGPT: An Agent-Based LLM Framework for the Design of Additively Manufactured Structural Alloys in Extreme Environments AMMap Library of Additive Manufacturing Design, Alloy Discovery, and Path Planning Anomaly Detection Via In-Situ Monitoring and Machine Learning Application of Multi-Physics Simulations and Machine Learning to Predict Spatter in Laser Powder Bed Fusion Can Machine Learning Predict the Liquidus Temperature of Binary Alloys?

Comprehensive Analysis of 316L Samples Fabricated via Directed Energy Deposition: Integrating Simulation With Microstructural and Mechanical Evaluations Digital Shadow Model Reference Control for Directed Energy Deposition Effect of Interpass Temperature on the Residual Stress Evolution in a Nickel-Aluminum Bronze Wire-Arc Additive Manufacturing Build Evaluating Absorptivity from Surface Temperature Measurements of Tracks Produced by Direct Laser Metal Deposition Finite Element Analysis of Deposition Strategies in Dissimilar Metal Additive Manufacturing Finite Element Analysis of Porous Implants Used for Forearm Free Flap Implant Full-Field Crystal Plasticity Surrogate Modeling for Rapid Defect Assessment in AM Materials G-124: Controlling Microstructure and Defect Through Physics-Informed Machine Learning in Laser Powder Bed Fusion Process G-126: Mathematical Quantification of Meniscus Fluctuations and Asymmetries in a Medium-Thin Slab Mold G-59: A Deep Learning Framework for Predicting Surface Deformation of Alloys Under Uniaxial Tensile Loading at Microscopic Length Scale G-60: A Machine Learning-Based Approach for Process Optimization in Laser Based 3-D Printing of High-Performance Al-Alloys G-61: A New Fast Solidification Cracking Indexing Tool for Metallic Alloys G-62: Additive Manufacturing Guided with High-Speed Photography and Machine Learning G-63: AM Microstructure Image Prediction Using Dimension Reduction G-64: Controlling Bubble Transport with External Magnetic Fields in Additive Manufacturing G-65: Development of a Steady-State 3D Heat Transfer and Materials Flow Model for Multi-Layer Additive Friction Stir Deposition G-66: Effect of Nucleation Model and Data Resolution on Cellular Automata Texture Strength Prediction G-67: Elastoplastic Thermomechanical Simulation of Powder Bed Fusion Incorporating Isotropic Strain Hardening and Cyclic Hardening/Softening Effects: A Comprehensive Approach G-69: Gaussian Process Regression Modelling and Texture Control During Hot Deformation of Additively Manufactured Maraging Steels G-70: Integrating CAFE with MOOSE for Microstructure Evolution Analysis in 316L Stainless Steel 3D Printing Process G-71: Large Language Models for Distilling Knowledge in Additive Manufacturing G-72: LLM Agents for 3D Printing Error Detection and Correction G-73: Local Stress Analysis of Ti5553 Lattice Structures Under Mixed Mode Stresses G-74: Machine Learning Guided Prediction of Printability During Additive Manufacturing G-75: Mathematical Study of Partial Blockage of SEN in Specific Zones on Flow Patterns in the Mold G-76: Mechanical Evaluation of Nested Structures Using Finite Element Analysis G-77: Microstructural Investigation and Numerical Analysis and Observation of Additively Manufactured Anti-Tetra-Chiral 316L Stainless Steel Samples G-78: Microstructure Prediction in Laser Powder Bed Fusion via Physics-Based Modeling and In-Situ Sensor Data Fusion G-79: Multi-Objective Study on Optimization of WAAM Parameters for Optimal Material Properties G-80: Multi-Phase-Field Modeling and High-Performance Simulations for Grain Structures Depending on Scanning Strategy During PBF Additive Manufacturing G-81:Quantifying the Characteristics of Pore Features Using Gaussian Process Machine Learning in LPBF Process Parameter Space G-82: Sensing-Based AM Process Mapping to Improve Reliability G-83: Simulation of Melt Pool Dynamics in Wire-Based and Powder-Based Directed Energy Deposition G-84: Towards a Fully Predictive Additive Manufacturing Module G-85: Understanding Structure-Property Interplay in 3D Printed Gyroid TPMS Lattices Generative Property Optimization of Stochastic Microstructures Hierarchical Machine Learning Framework for Optimizing Material Properties.

High-Fidelity Numerical Simulation of Droplet-Powder Bed Interactions in Binder Jet Additive Manufacturing.

High Fidelity Modeling of Laser Absorptivity and Molten Pool Geometry During Powderbed Fusion Processes of Ti64Al4V with the Stationary and Moving Laser Beam Sources Hyperspectral In-Situ Process Monitoring with High-Speed Infrared Pyrometry, Eddy Current Testing, and Machine Learning, for Predictive Analysis of AM Part Properties Impact of Infill Density and Raster Angle on 3D Printed High Impact Polystyrene (HIPS) Tensile Behavior Interpretable Machine Learning Approach for Exploring Process-Structure-Property Relationships in Metal Additive Manufacturing Machine Learning-Driven Predictions of Material Printability in Laser Powder Bed Fusion Machine Learning Enabled Process Optimization During 3D Printing of Tablets Mechanical Behavior of Additively Manufactured Metamaterials Under Dynamic Load Melt Pool Width Prediction with Machine Learning In Selective Laser Melting Micromechanical Modeling Exploration of Microstructure-Properties of Additively Manufactured Pure Tantalum Microstructure Evolution and the Influence on Material Properties in Additive Manufacturing Modeling and Simulation of the Shock Response of Additively Manufactured High-Performance Steel Modeling Fatigue Crack Initiation and Propagation Life in Additively Manufactured Alloys Across Fatigue Regimes Modeling GMA-DED Bead and Layer Geometry for Defect Elimination On the of Rapid Solidification in Additive Manufacturing Conditions by Combining Multiscale Simulations and In-Situ Monitoring Techniques Performance Optimization of Additively Manufactured β-TI5553 Alloy Lattice Structures: A Methodical Approach Integrating Topology and Strut-Level Microstructure Physics-Based and Data-Driven ICME for Metal Additive Manufacturing: from Feedstock to Process Optimization Preventing Substrate Distortion Using Hybrid Additive and Subtractive Approach Quantification of Defects in Additively Manufactured Steel Using Unsupervised Machine Learning Real-Time Detection of Keyhole Pore Generation in Laser Powder Bed Fusion Via a Multi-Sensor System and Physics-Informed Machine Learning Thermo-Mechanical Modeling and Validation of Residual Stress During Metal Laser Powder Bed Fusion and Post-Build Stress Relief Heat Treatment Processes Questions about ProgramMaster?

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