Theses and Dissertations

Advisor

Barrett, Christopher D.

Committee Member

Dickel, Doyl E.

Committee Member

Mun, Sungkwang

Date of Degree

5-16-2025

Original embargo terms

Visible MSU Only 1 year

Document Type

Graduate Thesis - Campus Access Only

Major

Mechanical Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Michael W. Hall School of Mechanical Engineering

Abstract

Magnesium is a strong and lightweight material with the potential to be used for weight reduction in various industries. However, manufacturing magnesium parts is difficult because magnesium is brittle at room temperature. Understanding the underlying deformation mechanisms in magnesium is critical to improving its ductility. In this work a Rapid Artificial Neural Network (RANN) potential was used to perform a Molecular Dynamics (MD) simulation in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to study twin formation and growth as well as interactions between different twin modes in a magnesium bicrystal. This work offers insight into the mechanisms of plastic deformation in magnesium.

Sponsorship (Optional)

NSF Award Number: 2237217

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