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Process monitoring for temporal-spatial modeling of laser powder bed fusion (L-PBF) based metal additive manufacturing
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Shaurya, Animek. Process monitoring for temporal-spatial modeling of laser powder bed fusion (L-PBF) based metal additive manufacturing. Retrieved from https://doi.org/doi:10.7282/T34F1V5S

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TitleProcess monitoring for temporal-spatial modeling of laser powder bed fusion (L-PBF) based metal additive manufacturing
NameShaurya, Animek (author); Ozel, Tugrul (chair); Baykal-Gursoy, Melike (internal member); Guo, Weihong (internal member); Rutgers University; School of Graduate Studies
Date Created2018
Other Date2018-05 (degree)
SubjectIndustrial and Systems Engineering, Lasers--Industrial applications
Extent1 online resource (xii, 88 p. : ill.)
DescriptionLaser powder bed fusion (L-PBF) is an additive manufacturing process which can produce nearly fully dense parts with complex geometry by using laser power which follows layer-to-layer scanning strategies on powder material with pre-specified powder layer thickness. In L-PBF, the powder material is fully melted and then solidified to achieve the above target. It offers high flexibility in manufacturing complex shapes reducing high tooling cost with its simple and quick setup and providing wide range of high strength alloys to work with. The parts fabricated through L-PBF are widely used for industrial applications such as in automobile, aerospace and medical industries. Various process parameters and scan strategies affect the build quality and structural integrity of the finished parts. In-process data analytics and statistical monitoring techniques are required to detect meltpool area, localize material spatter and control the process. High speed video imaging provides some process insight for identifying meltpool and spatter and can be integrated into process monitoring and control for L-PBF advanced manufacturing process. In this thesis, we studied the use of high speed camera videos for in-situ monitoring of L-PBF of nickel alloy 625 to detect meltpool, spatter, and over melting regions to improve the process control capability. The quantities that can be measured via in-situ sensing can be referred to as process signatures and can represent the source of information to detect possible defects. The video images are processed for temporal-spatial analysis by using principal component analysis and T2 statistics for identifying the history of pixel intensity levels through the process monitoring. These results are correlated as over melting and spatter regions. The results obtained from these studies will provide information about the process parameters which can be used for further validation of modelling studies or for industrial purposes.
NoteM.S.
NoteIncludes bibliographical references
Noteby Animek Shaurya
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T34F1V5S
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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