|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Slip Band Characterization with Microtensile Testing Using Digital Image Processing
||Anthony Lombardi, Elim Schenck, Subhasish Malik, Ajit Achuthan, Sean Banerjee, Natasha Kholgade Banerjee
|On-Site Speaker (Planned)
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.