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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Aluminum Alloy Design Using Physics Informed Machine Learning
Author(s) Fatih G. Sen, Marat Latypov, Heath Murphy, Kyle Haines, Shruthi Kumar Raj, Aurele Mariaux, Sazol Das, David Anderson, Debdutta Roy, Yudie Yuan, Vishwanath Hegadekatte
On-Site Speaker (Planned) Fatih G. Sen
Abstract Scope Automotive manufacturers are increasingly using aluminum alloys to reduce the weight of vehicles and as a result improve their fuel efficiency/performance. Advanced aluminum alloys with targeted properties needs to be developed faster, especially with the introduction of electric vehicles. In the current work, we have developed a physics informed machine learning framework to explore 6xxx aluminum alloy chemistry space for targeted product performance criteria. Computational thermodynamics methods were used to estimate microstructure features on historical alloy design data. These features were integrated with machine learning (ML) methods to estimate strength, ductility, formability and corrosion performance of 6XXX aluminum alloys. We used multi-objective genetic algorithm optimization to identify alloy chemistries that met or exceed the performance targets for specific automotive applications. We conducted a lab trial for selected alloys and their properties were evaluated and compared with the ML model predictions. The framework developed enables accelerated design of aluminum alloys.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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