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Meeting MS&T23: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Topic Modelling Framework for Rapid Digestion of Additive Manufacturing Literature
Author(s) Benjamin M. Glaser
On-Site Speaker (Planned) Benjamin M. Glaser
Abstract Scope Structural topic modelling typically requires a hands-on iterative process of industry experts reading, labelling, and sorting texts executed by a panels of domain experts. We have developed a pipeline using Latent Dirichlet Allocation (LDA) to accelerate topic identification and Natural Language Processing (NLP) to generate representative topic labels to reduce cognitive overhead. The capabilities of this program will be demonstrated on a dataset of US patent office publications and journal articles relevant to additive manufacturing to compare trends in recent years. The focus of this component of the exploration will be on areas of focus for additive manufacturing machines, as in the patent literature, and developments in alloy design via journal publications.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Efficient Void Shape Optimization Using Deep Generative Convolutional Neural Networks
Informing Autonomous Processing via STEM-EELS Using Variational Autoencoders for Classification and Decision
Machine Learning Segmentation Methods for Fatigue Fracture Surface Defect Analyses
Microstructure Statistics for Property Prediction in Multifunctional Electrode Composites Using Random Forests
Multi-modal Image Registration for Materials Characterization
Nanoscale Metrology of Materials Studied by Advanced Electron Microscopy Imaging and Spectroscopy.
Out-of-Domain Prediction of Material Property Using Deep Learning
Phase Segmentation of Steel Microstructures via Semi Supervised Deep Learning
Predicting the Occurrence and Mechanism of Liquid Metal Embrittlement Using Machine Learning
Rapid Grain Segmentation From Grayscale Micrograph Through Computer Vision Method
Semi-automated Hierarchical Clustering Model for 4D-STEM Datasets
Structure-property Relationships Derived From Electron Microscope to Atomistic Simulations
The Conundrum of Ambiguous Feature Sets in Materials Informatics for Images
Topic Modelling Framework for Rapid Digestion of Additive Manufacturing Literature
Using Computer Vision to Cluster Fatigue Life Based on Small Crack Characteristics
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