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About this Symposium

Meeting MS&T25: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Sponsorship TMS: Additive Manufacturing Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Jing Zhang, Purdue University
Li Ma, Johns Hopkins University Applied Physics Laboratory
Charles R. Fisher, Office Of Naval Research
Brandon A. McWilliams, US Army Research Laboratory
Yeon-Gil Jung, Korea Institute of Ceramic Engineering & Technology
Scope This symposium will provide an excellent platform to exchange the latest knowledge in additive manufacturing (AM) modeling, simulation, and machine learning. Despite extensive progress in the 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.Modeling of microstructure evolution, phase transformation, and defect formation in AM parts
2.Modeling of residual stress, distortion, plasticity/damage, creep, and fatigue in AM parts
3.Machine learning (ML), artificial intelligence (AI), and data science (DS)’s applications to AM
4.Calibration and validation data sets relevant to models
5.AM process monitoring and defect quantification
6.Efficient computational methods using reduced-order models or fast emulators for process control
7.Multiscale/multiphysics modeling strategies, including any or all of the scales associated with the spatial, temporal, and/or material domains

Abstracts Due 05/15/2025

PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE


3D Surrogate Modeling of Elasto-Viscoplastic FFT Simulations for Porosity-Driven Fatigue Prediction in Additive Manufacturing
A crystal plasticity approach to predict fatigue life with respect to critical defects in additively manufactured AlSi10Mg
A Crystal Plasticity Finite Element approach to understand effect of acoustoplasticity on FCC and BCC structures
A Micromechanical Comparison of Wrought and Additively Manufactured Inconel 718 subject to High Strain Rates
A unified model for accurate prediction of powder densification and shape distortion in large-scale components using powder metallurgy hot isostatic pressing (PM-HIP)
Accelerating Cellular Automata Grain Structure Predictions via Surrogate Thermal Modeling in Laser Powder Bed Fusion
Accelerating Grain Structure Predictions via Surrogate Thermal Modeling in Laser Powder Bed Fusion
Addressing the Portevin-Le Chatelier Effect in IN 939 Additively Manufactured Nickel-Based superalloy
Application of Ductile Fracture Modeling to Complex, Additively Manufactured SS316L Structure
Cavity-induced bubbles and pore formation of laser direct energy deposited titanium alloy: influence of powder melting degree
Cellular Automata Modeling of Microstructure and Porosity Formation in Al-10Si Alloy Laser Powder Bed Fusion Process
Comparative Analysis of Data Augmentation Strategies for Defect Classification in Fused Deposition Modelling Additive Manufacturing using VGG-16
DEVELOPING A DIGITAL TWIN FOR METALS ADDITIVE MANUFACTURING
Enhancing additive manufacturing process parameter design by computational fluid dynamics
Explore Feedstock powders and binder systems using an open-source binder inkjet 3D printer
Extension of the Pass Scale Method for Simulating Laser Powder Bed Fusion Additive Manufacturing Microstructures
Global Sensitivity Process Diagrams to Visualize the Impact of Composition Variability on Laser-based Powder Bed Fusion of Nickel Alloy 718
In situ Modulation of Residual Stresses During Laser Powder Bed Fusion
Integration of additive manufacturing techniques in novel electric motor design and fabrication
Interpreting peak temperature distributions in laser powder bed fusion through surface geometry and simulated imaging
Investigating Mechanical Anisotropy in Additively Manufactured 316L Stainless Steel
Investigation of Argon Gas Flow and Spatter Dynamics in Laser Powder Bed Fusion Using the Aconity MIDI System
Machine Learning-Aided Optimization for Laser-Based AM: Poweder selection
Melt Pool Plume Behavior in Laser Powder Bed Fusion
Microstructure design using kinetic model of the δ-ferrite to γ-austenite phase transformation in 17-4 PH stainless steel LPBF
Modeling Powder Sintering Process using Lattice Boltzmann Method
Modelling the Effects of Composition Variation and Heat Treatment on Microstructure of TiAl6V4 Produced Via Laser Powder Bed Fusion
Multi-fidelity framework to predict the melt pool characteristics for laser powder bed fusion of Inconel 718
Multi-physics simulation of directed energy deposition with blended materials
Net Shape Metal Additive Manufacturing via Polymer Bound Energetics in a Non-Inert Environment
Neural Network-Based Optimization of Stepover Distance for Wire-Arc Additive Manufacturing
Prediction optimal parameters for Wire-ARC DED Welding using multilayer-perceptron trained on synthetic data.
Probabilistic metrics for validation of grain growth models
Process monitoring in SolidStir® AM for Process Control and Quality Assurance
Robust additive manufacturable Ni superalloys designed by the integrated optimization of local elemental segregation and cracking susceptibility criteria
Shallow Neural Network Informed Dwell Time Selection for Thermal History Control in Laser Hot Wire Thin-Walled Parts
The Melt Pool Spatter Problem in Additive Manufacturing
Thermodynamic Modeling to Guide Process Optimization through Minimization of Sigma Phase Formation in Graded Stainless Steel – Vanadium Structures


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