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Physics-based simulation modeling and optimization of microstructural changes induced by machining and selective laser melting processes in titanium and nickel based alloys
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Arisoy, Yigit Muzaffer. Physics-based simulation modeling and optimization of microstructural changes induced by machining and selective laser melting processes in titanium and nickel based alloys. Retrieved from https://doi.org/doi:10.7282/T3MK6FX9

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TitlePhysics-based simulation modeling and optimization of microstructural changes induced by machining and selective laser melting processes in titanium and nickel based alloys
NameArisoy, Yigit Muzaffer (author); Ozel, Tugrul (chair); Jeong, Myong K (internal member); Coit, David W (internal member); Scherer, George W (outside member); Guo, Changsheng (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2016
Other Date2016-01 (degree)
SubjectIndustrial and Systems Engineering, Surfaces (Technology), Microstructure
Extent1 online resource (xxvii, 358 p. : ill.)
DescriptionManufacturing processes may significantly affect the quality of resultant surfaces and structural integrity of the metal end products. Controlling manufacturing process induced changes to the product’s surface integrity may improve the fatigue life and overall reliability of the end product. The goal of this study is to model the phenomena that result in microstructural alterations and improve the surface integrity of the manufactured parts by utilizing physics-based process simulations and other computational methods. Two different (both conventional and advanced) manufacturing processes; i.e. machining of Titanium and Nickel-based alloys and selective laser melting of Nickel-based powder alloys are studied. 3D Finite Element (FE) process simulations are developed and experimental data that validates these process simulation models are generated to compare against predictions. Computational process modeling and optimization have been performed for machining induced microstructure that includes; i) predicting recrystallization and grain size using FE simulations and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, ii) predicting microhardness using non-linear regression models and the Random Forests method, and iii) multi-objective machining optimization for minimizing microstructural changes. Experimental analysis and computational process modeling of selective laser melting have been also conducted including; i) microstructural analysis of grain sizes and growth directions using SEM imaging and machine learning algorithms, ii) analysis of thermal imaging for spattering, heating/cooling rates and meltpool size, iii) predicting thermal field, meltpool size, and growth directions via thermal gradients using 3D FE simulations, iv) predicting localized solidification using the Phase Field method. These computational process models and predictive models, once utilized by industry to optimize process parameters, have the ultimate potential to improve performance of products in their service life.
NotePh.D.
NoteIncludes bibliographical references
Noteby Yiğit Muzaffer Arisoy
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3MK6FX9
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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