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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
Presentation Title Training Data-driven Machine Learning Models Using Physics Simulations: Predicting Local Thermal Histories in Additive Manufactured Components
Author(s) Michael Groeber, Karthik Giriprasad
On-Site Speaker (Planned) Michael Groeber
Abstract Scope The processing parameter space in additive manufacturing (AM) is prohibitively large – driving the need for ICME processing tools. In powder bed fusion processes (e.g. LPBF or EBM) printing parameters such as beam power, focus, raster speed, and raster path affect thermal and mechanical stress states. Mapping influences of these parameters is daunting, preventing design of customized microstructures theoretically achievable via AM. Energy input time series, listing relative distances, timings and intensities of energy input, represent the local scan path at locations in AM components. We investigate processing these time series through a 1D, deep convolutional neural network (CNN) to predict local thermal histories. A fast-acting analytical model predicts thermal histories for training the CNN. We aim to show even efficient thermal models can be replaced by machine learning models, derived from novel representations of local energy input. These models provide additional promise when trained against advanced physics.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Bayesian Framework for Materials Knowledge Systems
Artificial Intelligence for Material and Process Design
Automated Data Curation for Electron Microscopy Using the Materials Data Facility
Combining Machine Learning and ICME for Alloy Development
Computational Classification, Generation and Time-evolution Prediction of Alloy Microstructures with Deep Learning
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Discovering and Navigating Gaps and Connections in Data for Materials Design
Gaps and Barriers to the Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows
Gaps, Limitations, and Pitfalls of Materials Informatics
Improved Performance of Automatic Characterization of Steel Microstructure by Machine Learning Architecture
L-18 (Invited): Multi-fidelity Surrogate Assisted Framework for Prediction and Control of Meltpool Geometry in Additive Manufacturing Processes
L-19: Data-driven Hard-magnetic Materials Selection for AC Applications by Multiple Attribute Decision Making
L-20: Data Driven Prediction of Crystallographic Attributes of Small Molecules Using Various Molecular Fingerprints
L-21 (Digital): Deep Learning Image Analysis for Lattice Material Qualification
L-22: Effect of Microtextured Regions on the Deformation Behavior of Titanium Alloys Submitted to Monotonic and Cyclic Loadings Investigated using FFT-EVP Simulations
L-25: Multi-class Inclusion Identification via Machine Learning of Multilevel Image Features
L-26: Prediction of Temperature after Cooling in Coils Using Machine Learning and Finite Element Method
L-27: Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-informed Data-driven Modeling with Experimental Validation
Machine Learning-directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-confined Regime
Machine Learning for Materials Science: Open, Online Tools in NanoHUB
Machine Learning to Predict Oxidation Behavior of High-temperature Alloys
Magicmat (MAterials Genome and Integrated Computational MAterials Toolkit) and Its Application for Thermoelectric Materials Design
Polymer Informatics: Current Status & Critical Next Steps
Predicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network
Prediction of Steel Micro-structure by Deep Learning Using Database of Thermo-dynamics and Phase Field Model
Reduction of Uncertainty in a First-principles-based CALPHAD-type Phase Diagram via Sequential Learning of Phase Equilibrium Data
Relating Microstructure Features to Response Using Convolutional Neural Networks
Steel Development and Optimization Using Response Surface Models
The MGI and ICME
Training Data-driven Machine Learning Models Using Physics Simulations: Predicting Local Thermal Histories in Additive Manufactured Components
Uncertainty Quantification and Propagation in ICME Enabled by ESPEI
View on Data Ecosystem of Materials

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