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Meeting Materials Science & Technology 2016
Symposium Modeling of Multi-Scale Phenomena in Materials Processing and Advanced Manufacturing
Sponsorship
Organizer(s) Adrian S. Sabau, Oak Ridge National Laboratory
Anthony Rollett, Carnegie Mellon University
Laurentiu Nastac, The University of Alabama
Mei Li, Ford Motor Company
Ashley Spear, University of Utah
Scope The purpose of the symposium is to present the development of computational techniques for the modeling of multi-scale phenomena in materials processing and advanced manufacturing processes such as additive manufacturing. The submitted papers should address the new application of conventional techniques or new, more efficient, numerical methods for the solution of problems that involve multiple-scale phenomena. Typical topics may include solution algorithms for coupling of models that describe microscale and macroscale phenomena, deterministic and stochastic models, phase field simulations, direct numerical simulations of microstructure, mass transport, stress evolution during solidification, and fluid dynamics effects. In addition to theoretical studies, experimental efforts that generate data to validate the models are encouraged; as are applications of these techniques to solve actual complex problems faced by industry in developing new materials and new processes. Topics may pertain to phenomena related to any material or metallurgical processes.
Abstracts Due 03/31/2016
Proceedings Plan Definite: MS&T all conference proceedings CD
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Arresting Deleterious Particle Growth: A Phase Field Study of Mechanisms
Bayesian Calibration of Surrogate Models for Uncertainty Quantification in Additive Manufacturing
Biased Flows in Slab Molds Induced by Slide Gates and Their Correction through SEN Design
Calibration of Constitutive Models Used for Powder Compaction Process: Different Strategies and Related Difficulties
Fast Determination of Critical Buckling Condition under Welding with Computational Analysis
Image Driven Machine Learning Methods for Microstructure Recognition
Implementation of a Coupled Read-Shockley Q-state Monte Carlo into the SPPARKS Framework to Simulate Magnetic Field Influence on Textures
Lattice Correspondence Analysis of {10-12}〈10-1-1〉 Twinning in Magnesium Using Atomistic Simulations
Microscale Modeling of Additive Manufactured Ti-6Al-4V’s Response and Failure
Microstructure Predictions from a Macro-scale Casting Model
Modelling the Influence of Proximal Phases in Nano-indentation of Metallic Glass Matrix Composites
Multiscale Modeling of Additive Manufacturing
Quantifying the Material-model Form Error for Welded and Additively-manufactured Structures Using Multiscale a Posteriori Error-estimation Techniques
Secondary Steelmaking CAS-OB Process Kinetic Model
Thermal and Flow Behavior of Melt Pool during Selective Laser Melting of AlSi10Mg Powder in Point Exposure Laser Scan Pattern


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