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Meeting MS&T23: Materials Science & Technology
Symposium Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
Sponsorship TMS: Additive Manufacturing Committee
TMS: Computational Materials Science and Engineering Committee
TMS: ICME 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
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) and artificial intelligence (AI) 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 05/08/2023

3-Dimensional Microstructure Characterization of Laser Powder Bed Fusion IN625 and IN718
3D Deep Learning for Porosity Analysis in Additive Manufacturing
A-1: 3D Printed Ceramics for Solid-state Battery Components
A-22: Enhancing Material Properties in Multi-Material 3D Printing: Exploring In-Situ Mixing and Material Gradation
A-23: Mechanical Ventilator Prototype Using 3D Printed Components
A-2: Deep Learning Assisted Material Structure Property Linkage of 3D Printed AlSi10Mg Alloy
A-3: Density Functional Theory Based Methods for Predicting Interfacial Strengths in Thermal Barrier Coatings with MXene Using Spark Plasma Sintering
A-4: Developing Virtual Reality Models to Simulate Additive Manufacturing Process
A-5: Extrusion Based 3D Printing of Silicon Carbide
A-6: Inkjet 3D Printing of Biodegradable Materials
A-7: Modeling Laser Heating Phenomenon in Refractory Metal Powder Bed Fusion Process
A-8: Simulation of Shell Thickness and Inclusions Trajectory in Casting Mold of Round Steel Billet Continuous Casting
A Molecular Dynamics Study on the Micro Cold Spray of Zinc Oxide Films
A Unified Treatment of Alloy Dependent Material Properties and Process Parameters for Accurate Solidification Simulations for AM Based on CALPHAD
Analyzing and Predicting Surface Roughness in Laser Powder Bend Fusion
Better Understanding of Cracking Phenomena in High-Strength Superalloys through Multiphysics Modeling in Additive Manufacturing
Computational and Experimental Study of Up-/Down-surface Characteristics of Sloped Samples in L-PBF Process
Effect of Size, Location, and Aspect Ratio of Pores on Ductility of PBF-LB Ti-6Al-4V: Experiments and Simulations
Examining the Effect of an Oxide Layer on the Deposition of Tantalum Films via Micro-Cold Spray
Finite Element Simulation Based on Constitutive Model of Cellular-structured Metals Produced by Additive Manufacturing
Gas Atomization of Mg-Zn-Ca-Mn Alloy Powder for Additive Manufacturing
Mechanical Properties of Truss-based Nanolattices: A Molecular Dynamics Study
Microstructure Evolution Simulation of Inconel 718 Superalloy during Laser Powder Bed Fusion (LPBF) Process
Modeling In-Situ Phase Transformation in Inconel 718 and EH36: A Study Using Phase Field and Phase Fraction Models
Multi-Model Monte Carlo Simulations of Mechanical Behavior of Additively Manufactured Metals
Open-source Numerical Simulations of Melt Pool Physics in Laser Powder Bed Fusion Processes
Physics-constrained, Inverse Design of High-temperature Strength Printable Aluminum Alloys with Low Cost and CO2 Emissions for High Demand Industries
Physics Informed Reduced Order Model for Directed Energy Deposition Simulations in MALAMUTE
Predicting Material Properties in Additive Manufacturing Using Acoustic Signatures and Machine Learning
Quantification of Carbide Pickup and Quality Control of SS 316L Manufactured via Binder Jet Printing
Quantification of Spatter Counts and Trajectories in Laser Powder Bed Fusion using Machine Learning Analysis of High Speed Imaging
Simulating the 3D Printing Process of Hydroxyapatite Powders
Simulation of Anisotropic Mechanical Behavior of Additively Manufactured Ti-6Al-4V Wall Structures using VPSC
The Effect of Disorder and Constitutive Material on the Mechanical Properties of Bioinspired Honeycombs
Use of Machine Learning to Identify Process-Structure-Property Relationships in PBF-LB AlSi10Mg
Utilizing Cellular Automata to Resolve Process Parameter to Microstructure Correlations in LPBF Additively Manufactured Parts

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