dc.contributor.advisor | Elwany, Alaa | |
dc.creator | Tapia Imbaquingo, Gustavo Andres | |
dc.date.accessioned | 2019-01-17T19:31:11Z | |
dc.date.available | 2019-01-17T19:31:11Z | |
dc.date.created | 2018-05 | |
dc.date.issued | 2018-05-04 | |
dc.date.submitted | May 2018 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/173574 | |
dc.description.abstract | Laser Powder-Bed Fusion processes capable of processing metallic materials are a set of relatively
new and emerging Additive Manufacturing technologies that offer attractive potential and
capabilities (e.g., design freedom, part consolidation and reduced material waste). Although they
provide an exceptional advantage that cannot be matched by other traditional manufacturing processes,
the path to widespread use of these new technologies still include some obstacles due to
the limited understanding and intricate problems that the manufacturing process presents, such as
low repeatability and low part quality compared to their conventional manufacturing counterparts.
This dissertation presents one of the first applications of different formal tools and frameworks
from a combination of scientific fields including Uncertainty Quantification, Statistics, Probability
and Data Science, into different problems within Additive Manufacturing Laser Powder-Bed
Fusion processes. Specifically, modeling techniques such as Gaussian Processes and generalized
Polynomial Chaos Expansions are employed to optimize porosity in printed parts, calibrate and
validate different computer simulation models, and identify processing regions for satisfactory
manufacturing. Proper analysis of these techniques is undertaken and its validation is successfully
presented such that informed and knowledgeable perspectives about the manufacturing process are
gained to better understand it. In turn, these new insights and understanding translate into improvement
and advancement of Additive Manufacturing, and contribute towards its further growth and
consolidation as a competitive and qualified technology within the manufacturing industry. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | additive manufacturing | en |
dc.subject | uncertainty quantification | en |
dc.subject | powder-bed fusion | en |
dc.subject | uncertainty propagation | en |
dc.subject | polynomial chaos expansions | en |
dc.subject | Gaussian processes | en |
dc.subject | Bayesian statistics | en |
dc.title | Quantifying and Reducing Uncertainty in Metal-Based Additive Manufacturing Laser Powder-Bed Fusion Processes | en |
dc.type | Thesis | en |
thesis.degree.department | Industrial and Systems Engineering | en |
thesis.degree.discipline | Industrial Engineering | en |
thesis.degree.grantor | Texas A & M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.level | Doctoral | en |
dc.contributor.committeeMember | Arroyave, Raymundo | |
dc.contributor.committeeMember | Bukkapatnam, Satish | |
dc.contributor.committeeMember | Sang, Huiyan | |
dc.type.material | text | en |
dc.date.updated | 2019-01-17T19:31:12Z | |
local.etdauthor.orcid | 0000-0003-4647-663X | |