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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Computational Techniques for Multi-Scale Modeling in Advanced Manufacturing
Presentation Title Predicting Mechanical Performance in Additive Manufacturing Components Using Deep Learning
Author(s) Kyle L. Johnson, John M. Emery, Demitri Maestas, Matthew D. Smith, Carianne Martinez, Mircea Grigoriu
On-Site Speaker (Planned) Kyle L. Johnson
Abstract Scope Across a range of disciplines, Deep Learning (DL) has shown tremendous success in discovering features and patterns within input data by detecting structures and hierarchies. This talk will present results of a recent effort to utilize DL algorithms to predict microstructure-dependent mechanical performance in synthetic metal coupons representative of additively manufactured material. To train the DL network, a large database of synthetic data was developed based on physical material measurements of AlSi10Mg. First, finite element meshes of tensile specimens containing voids were generated based on statistical distributions measured through high-resolution X-ray tomography. The samples were then loaded in tension, with different pore distributions leading to strain localization in different regions over a range of peak loads. The resulting data was used to train a 3D Convolutional Neural Network to predict mechanical properties in different stress states and geometries. Progress to date will be discussed, along with challenges and future work.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, Computational Materials Science & Engineering

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Parametric Study of Grain Size and Its Volume Fraction Effect on Heterogeneous Materials Mechanical Properties
Computational Modeling of Nanoparticles Dispersion in Hybrid Process of Ink Jetting and Laser Powder Bed Fusion
Computational Multi-Scale Modeling of Segregation and Microstructure Evolution during the Solidification of A356 Ingots Processed via a 2-Zone Induction Melting Furnace
Effect of Nozzle Injection Mode on Initial Transfer Behavior of Round Bloom
Fluid Dynamics Effects on Microstructure Prediction in Single-Laser Tracks for Additive Manufacturing
In Situ and Operando Synchrotron Experiments for Additive Manufacturing Model Validation
Investigation of Powder Spattering in Laser Powder Bed Fusion through Multi-physics Modeling and High-speed Synchrotron X-ray Imaging
Machine-learning Informed Design of High-strength Gradient Metals for Additive Manufacturing
Microstructural Evolution and Defect Formation During Pulsed and Continuous Selective Laser Melting
Microstructure Based Modeling of Friction Stir Welded Joint between Dissimilar Metals Using Crystal Plasticity
Modeling Material Behavior during Continuous Bending Under Tension for Inferring the Post-necking Strain Hardening Response of Ductile Sheet Metals: Application to Dual-phase Steels
Modeling the Role of Local Crystallographic Correlations in Microstructures of Ti-6Al-4V Using a Lamellar Visco-plastic Self-consistent Polycrystal Plasticity Formulation
Multiphysics Simulation of Microstructure Evolution in Selective Laser Melting of AlSi10Mg
Multiscale Crystal Plasticity in Integrated Computational Materials Engineering
Particle Resolved Simulation of Laser Powder-bed Fusion Including Metal Evaporation and Vapor Plume Dynamics
Phase-field Modeling of The Evolution Kinetics of Porous Structure During Dealloying of Binary Alloys
Predicting Mechanical Performance in Additive Manufacturing Components Using Deep Learning
Smoothed Particle Hydrodynamics based approach for 3D Modeling of Linear Friction Welding Process
Study on the In-mold Flow Behavior Driven by a Subsurface Electromagnetic Stirring for IF Steel Slab Casting
Synchrotron Calibrated Lagrangian Particle Tracking of Melt-pool Ejections during Laser Powder Bed Fusion

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