Numerical Framework for Selective Laser Melting Processing of Thermoelectric Materials
General Material Designation
[Thesis]
First Statement of Responsibility
Rammos, Panagiotis
Subsequent Statement of Responsibility
LeBlanc, Saniya
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
The George Washington University
Date of Publication, Distribution, etc.
2020
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
63
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
M.S.
Body granting the degree
The George Washington University
Text preceding or following the note
2020
SUMMARY OR ABSTRACT
Text of Note
This work presents a numerical framework for predicting selective laser melting processing of thermoelectric materials. The developed framework combines the steady and transient temperature field predictions of the analytical Eagar-Tsai model and a more detailed finite element model, respectively, to establish a fundamental understanding of the process-temperature field relationship, achieve a fast process parameter space exploration and quantify process-affecting temperature field characteristics. To explore the manufacturing feasibility of thermoelectric materials with selective laser melting, a case study of bismuth telluride processing is presented. The reliability of predicted temperature fields and their associated melt pool dimensions is assessed, for given sets of processing parameters. The process parameter space is sampled with the Eagar-Tsai model to obtain the predicted melt pool dimensions. Then, three processing scenarios are sampled with the more computational demanding finite element model, to obtain the process thermal history. The melt pool evolution during processing is studied. Spatial and temporal temperature gradients are quantified and their contribution to qualities of manufactured parts is discussed. Results from both models are compared to highlight the importance of neglected physics. Future steps are proposed to increase the computational accuracy of the presented framework and further study its effectiveness with experimental trials.