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Meeting MS&T21: Materials Science & Technology
Symposium 2021 Undergraduate Student Poster Contest
Presentation Title Machine Learning Approaches to Predict Properties from Microstructure Images in Ceramic-Metal Composites
Author(s) Hugh B. Smith, William Huddleston, Laura Bruckman, Alp Sehirlioglu
On-Site Speaker (Planned) Hugh B. Smith
Abstract Scope Electrical conductivities of composites of Li4Ti5O12 anode and Ni current collector particles for structural battery applications were predicted from SEM microstructure images. Further, microstructural features contributing the most to conductivity for different samples could be identified. Principal component analysis (PCA) was performed on voronoi, nearest neighbor, size, and skeleton distributions of the microstructural features, and logistic regression and linear discriminant analysis models were fit to the scores of the principal components (PCs) to classify the images as low or high conductivity. Accuracies exceeded 87%, and the most important PCs were identified. Convolutional neural networks (CNN) were then used for classification and led to accuracies above 98%. Class activation maps were created from the CNN models for testing images. These highlight which microstructural features influenced conductivity predictions. Regression was attempted on the PCs using multiple linear regression and general additive models with little success. Regression attempts with CNN were unstable due to the sparsity of the data set but yielded responses that were usually 4x off the observed responses on average with values for r2 usually exceeding 0.7.


Composition and Physicochemical Properties of Typical Waste Cooking Oil
Effect of Carbon Stoichiometry on the Heat of Formation of Hafnium Carbides
Effect of Modifier Cation Size on the Bulk Structure and Nickel Speciation in Alkali Borosilicate Glasses
Electron Cloud Migration Effect-induced Lithiophobicity/Lithiophilicity Transformation for Dendrite-free Lithium Metal Anodes
Enhanced Mechanical Properties in a 4140 Steel by "In-House" Intensive Quench
Experiential Study on Critical Stress Intensity Factor of Carbon Nanotube Filled Epoxy
Exploring the Liquid Phase Exfoliation of Two-Dimensional Bilayered Vanadium Oxide in Aqueous Media for Li ion Batteries
High Temperature Mechanical Properties of TiB2-WC-SiC Materials
Highly Crystallized Prussian Blue with Enhanced Kinetics for Highly Efficient Sodium Storage
Investigation of Embedded Metallic Components on 3D Printed Ceramic Structures
Machine Learning Approaches to Predict Properties from Microstructure Images in Ceramic-Metal Composites
Mechanical Behavior of Automotive Structural Steels in the Vicinity of the Ductile-brittle Transition
Mechanical Property Assessment of Silicon Carbide Fiber-reinforced Epoxy-matrix Composites
Optimal Integration of TiO2-Coated Gold Nanostars for Enhancement of Photocatalytic Water Reduction
Perovskite Film Formation for Solar Cell Absorbers: Effects of Substrate Modification
Pressure Optimization of Fast-Moving Silicon MEMS Micromirrors
Processing and Properties of (Ta, Nb, Hf, Ti)C
Rheological Characterization of Highly Loaded Alumina-Polymer Suspension for Thermal Paste 3D Printing
The Dynamic Performance of Wearable Sensors with Flexible Silver Ink
The Mechanisms of Micro-fine Titanaugite Enter to Ilmenite in the Flotation and Depression Behavior of Sodium Silicate
Thermal Analysis of Sodalite-immobilized Iodine-129 Caustic Scrubber Slurry
ZrB2 Aqueous Slurry Development for DIW Additive Manufacturing

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