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An Automated, Parameterized Model of Maize Stalk Strength via Machine Learning

Year: 2023

Presenter Name: Ryan Hall

A fully parameterized model of the maize stalk morphology was created using machine learning techniques. A database of 1000 CT scans of maize stalks served as the training data. The model consists of over 50 geometric parameters and 14 physical material properties. The parameterization scheme allows independent control of each physical feature of the stalk. This was accomplished by linking key landmarks with empirical eigenfunctions to capture morphological patterns in the transverse and axial directions. The parameterized model was validated by comparing results of models based on actual maize stalk shapes with parameterized counterparts in multiple loading scenarios: axial, torsion, bending, transverse compression, flexural stiffness, and ultimate bending strength. The resulting model accurately captures behavior of actual stalks, can be "fit" to any specimen, and can be used to perform sensitivity and optimization studies. The model creation, validation, and preliminary sensitivity results will be presented.
University / Institution: Brigham Young University
Type: Oral
Format: In Person
SESSION C (1:45-3:15PM)
Area of Research: Engineering
Faculty Mentor: Douglas Cook
Location: Union Building, DEN (2:25pm)