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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title 3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
Author(s) Jason Fowlkes, Robert Winkler, Eva Mutunga, Juergen Sattelkow, Philip Rack, Harald Plank
On-Site Speaker (Planned) Jason Fowlkes
Abstract Scope Methods to 3D print materials at the nanoscale are few. This has limited the fabrication of nanoscale materials to mostly in–plane features and elements. This is especially true for nanomaterials synthesized using conventional nanoelectronic fabrication techniques where layer–by–layer fabrication is paramount. Focused electron beam induced deposition (FEBID) is a promising 3D nanoprinting method which, until recently, was limited to a trial and error approach rendering the method ill–suited for the reliable, on–demand printing of complex geometries. An integrated capability of experimental calibration, computer–aided design and simulation will be presented that solves this problem, at least for the case of complex 3D mesh style objects. Further, the integrated capability was used to determine the cause of a common 3D geometric distortion observed for relatively tall deposits. Beam–induced heating was revealed as the distortion mechanism leading to the development of a model–based compensation solution.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
Deep Learning and MC-X ray, toward Automatic Sample Segmentation
Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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