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 Symposium
Meeting MS&T24: 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 in Indianapolis
Li Ma, Johns Hopkins University Applied Physics Laboratory
Brandon A. McWilliams, US Army Research Laboratory
Yeon-Gil Jung, Korea Institute of Ceramic Engineering & Technology
Charles R. Fisher, Naval Surface Warfare Center - Carderock
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 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 05/15/2024
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

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
Analytical Prediction of Texture of Multi-Phase Material in AM
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
Crystal Plasticity Investigation of Effect of As-Printed Dislocation Densities on Mechanical Anisotropy of Additive Manufacturing 316L Stainless Steel
Development of A Customized Open-Source Inkjet 3D Printer
Efficient Laser Powder Bed Fusion Textured Solidification Models
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
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
Melt Pool Geometry Analysis of a Ti-W Gradient Material Using In-Situ Monitoring and FEA in a DED AM System
Micro Cold Spray of Partially Sintered Zinc Oxide Nanoparticle Agglomerates
Micromechanical Modeling Exploration of Microstructure-Properties of Additively Manufactured Pure Tantalum
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

Questions about ProgramMaster? Contact programming@programmaster.org