Date of Award
12-2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
Committee Chair/Advisor
Dr. Enrique Martinez Saez
Committee Member
Dr. Marian Kennedy
Committee Member
Dr. Garrett Pataky
Abstract
By the 1990s, computational methods were beginning to be used to study dislocation density in mental casting. Prior to that time research was based heavily on laboratory studies. This move was encouraged by the need to overcome limitations that experimental approaches had, including high costs and time-consuming processes, and needs of researchers to understand atomic-scale phenomena. Computational techniques allow complex materials to be simulated in cost effective way and can provide insight into behaviour that could never be determined through experimental methods alone.
This work utilized molecular dynamics (MD) simulations along with machine learning approaches to examine the dislocation density as a function of compositional variations in 316 stainless steels (316SS). 316SS is well-characterized experimentally and is important economically. Slight deviations in both Ni and Cr contents of our samples enabled us to establish their role on the dislocation density. This study included both nominal 316SS compositions like 10 at% Ni and 16 at% Cr, as well as those that were out of the standard 316SS range. By incorporating MD simulations with machine learning, we developed a predictive model that can estimate the dislocation density as a function of both the casting conditions and material composition. More specifically we investigated how changes in parameters such as Ni and Cr concentrations alter the dislocation density within 316SS after solidification. The MD approach has further been very informative for optimizing the casting parameters in some usually applied industrial alloys, improving efficiency while keeping the material properties guaranteed.
Metals manufacturing techniques such as casting, play a pivotal role in U.S. industry. Process parameters influence the mechanical performance and structural integrity of final products. Predicting and controlling dislocation density during a casting process is essential for ensuring high-quality materials with superior properties. While experimental studies provide valuable insights, computational techniques offer an efficient and scalable alternative for exploring the complex dynamics of dislocation behavior.
Recommended Citation
Chitradurga Ranganath, Abhishek, "Prediction of Dislocation Density During Casting of 316 Stainless Steel Based Alloys as a Function of Chromium and Nickel Composition" (2024). All Theses. 4393.
https://open.clemson.edu/all_theses/4393
Author ORCID Identifier
C12016977
