Conference Tools for MS&T21: Materials Science & Technology

Help

About this Abstract
Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	Slip Band Characterization with Microtensile Testing Using Digital Image Processing
Author(s)	Anthony Lombardi, Elim Schenck, Subhasish Malik, Ajit Achuthan, Sean Banerjee, Natasha Kholgade Banerjee
On-Site Speaker (Planned)	Anthony Lombardi
Abstract Scope	The characteristics of slip band formation and its evolution on the specimen surface of metals and alloys during a microtensile test provides insight on the influence of various microstructural features on mechanical properties. In this presentation, we discuss two digital image processing approaches to derive slip band characteristics from raw video data of a microtensile test. Our first approach estimates slip line density by detecting lines using Hough, dividing the image region into cells, using Kanade-Lucas-Tomasi feature tracking to track each cell across the video, and counting lines in each cell. We evaluate Hough line detection in grayscale versus black-and-white images, and density detection using binning of lines into pixels versus measuring lengths of cell segments obtained using Cohen-Sutherland clipping. Our second approach matches Gabor filters representing oriented Gaussian-blurred sine functions of varying frequencies to the image for modeling probability distributions of finding various line densities in each video image.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects

Accelerating Discovery in Computational Materials Science Using CAMD

Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics

Characterization of Microscopic Deformation of Materials Using Deep Learning Methods

Considerations for Interpretability, Reliability, And Data-efficiency in Machine Learning Properties of Solid-state Materials

Data Science as Bridge – Materials Characterization and Modeling

Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos

Designing Alloys with Process-mapping AI Pre-trained on Empirical Knowledge

Developing Physics-based Descriptors for Property Prediction in Oxide Glasses

Learning Synthesis: Engineering Metal Nanoclusters for Specific Material Properties

Machine Learning in 2D Materials: Benchmarking Crystal Graph Based Convolutional Neural Network (CGCNN) for Open Databases

Machine Learning to Predict Mechanical Properties of Steel Alloys Based on Chemical Composition and Heat Treatment Process

Materials Graph Ontology for Improving the Standardization and Utilization of Materials Data

Molecular Dynamics Simulation Using Lagrangian Neural Networks

Multi-target Prediction of Concrete Engineering Properties Based on a Single Deep Learning Model

P3-18: Rashba Spin Splitting and Photocatalytic Properties of GeC−MSSe (M=Mo, W) Van Der Waals Heterostructures

P3-19: Thermo-mechanical Property Prediction of High-temperature Materials Using a Python Based Interface With Quantum Espresso

Predicting Glass Behaviour from Optical Microscopy Images Using Interpretable Machine Learning

Scalable Gaussian Processes for Predicting the Optical, Physical, Thermal, and Mechanical Properties of Inorganic Glasses Using Compositions for Large Datasets

Searching for New Ferroelectric Materials Browsing a High-throughput Phonon Database

Semantic Segmentation of Plasma Transferred Arc Additively Manufactured NiBSi-WC Optical Microscopy Images Using a Convolutional Neural Network

Slip Band Characterization with Microtensile Testing Using Digital Image Processing

There is No Time for Science as Usual

Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression

Questions about ProgramMaster? Contact programming@programmaster.org