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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 Steel Development and Optimization Using Response Surface Models
Author(s) Jun Hu, Rachael Stewart, Erik J. Pavlina, Grant Thomas, Alexander Duggan, Roel Van De Velde
On-Site Speaker (Planned) Jun Hu
Abstract Scope With more advanced and sophisticated technologies implemented into steel development, conventional empirical methodology is becoming more inefficient and undirected in this process. Response surface models are thereby introduced into this field to integrate ‘big data’ and computationally bridge inputs to outputs. In this work, a completed procedure will be presented to show training response surface models using different algorithms based on a steel chemistry and processing database with corresponding mechanical properties. Furthermore, optimization will be applied to mine feasible but undeveloped new steel possibilities from the well-trained response surface model. To validate the computation, a laboratory steel is processed, and the resulting mechanical properties are and compared with the computational results.
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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