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Prediction of surface texture parameters using machine learning in laser surface texturing
Descriptive
TitleInfo
Title
Prediction of surface texture parameters using machine learning in laser surface texturing
Name
(type = personal)
NamePart
(type = family)
Yang
NamePart
(type = given)
Lihang
NamePart
(type = date)
1995-
DisplayForm
Lihang Yang
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
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Ozel
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Tugrul
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Tugrul Ozel
Affiliation
Advisory Committee
Role
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chair
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Graduate Studies
Role
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school
TypeOfResource
Text
Genre
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theses
Genre
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ETD graduate
OriginInfo
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2020
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2020-10
Language
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(type = text)
English
Abstract
(type = abstract)
Laser surface texturing provides several benefits such as improved tribological behavior of the surface, reduced friction, increased anti-adhesive properties and improved wettability and lubrication applications. However, surface topography or surface texture resulting from laser processing and the relation between laser texturing parameters and surface texture parameters are not well understood. In the thesis paper, besides the statistical tool such as linear regression, Machine learning methods such as Artificial Neural Networks are utilized to generate a predictive modelling capability for the relationships between laser surface processing parameters and the resultant texture parameters on the scale-limited surfaces surveyed.
Subject
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Topic
Industrial and Systems Engineering
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_11069
PhysicalDescription
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application/pdf
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Extent
1 online resource (ix, 63 pages) : illustrations
Note
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M.S.
Note
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Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-8fb9-p877
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Yang
GivenName
Lihang
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2020-08-02 13:59:26
AssociatedEntity
Name
Lihang Yang
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
RULTechMD
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ETD
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windows xp
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1.4
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macOS 版本10.15.6（版号19G2021） Quartz PDFContext
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2020-09-10T18:42:00
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2020-09-10T18:42:00
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