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Predictive modeling and optimization in hard turning: investigations of effects on cutting tool micro-geometry
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Karpat, Yiğit. Predictive modeling and optimization in hard turning: investigations of effects on cutting tool micro-geometry. Retrieved from https://doi.org/doi:10.7282/T3HM58VC

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Uniform TitlePredictive modeling and optimization in hard turning: investigations of effects on cutting tool micro-geometry
NameKarpat, Yiğit (author); Ozel, Tugrul (chair); Elsayed, Elsayed (internal member); Boucher, Thomas (internal member); Cuitino, Alberto (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2007
Other Date2007-10 (degree)
SubjectIndustrial and Systems Engineering, Turning (Lathe work), Metal-cutting tools
Extentxxvi, 238 pages
DescriptionHard turning, which is turning of hardened parts into finished components, offers distinct advantages to manufacturers. It is favorable in terms of cost because it has the potential to eliminate the grinding process. Complex surfaces can be machined with a single machine. It is an environmentally friendly process because no cutting fluid is used. Its major drawback is rapid tool wear due to high temperatures and high stresses at the tool rake and flank faces. Short tool lives of expensive Polycrystalline Cubic Boron Nitride (PCBN) type of cutting tools hinder the economic advantage of hard turning. This research focuses mainly on the effects of cutting tool micro geometry on hard turning process. The goal is to develop a methodology for the selection of cutting tool micro geometry and machining parameters for hard turning process. To this end, existing analytical and experimental modeling techniques used in modeling of hard turning will be improved and new methodologies will be proposed.
In this research, firstly, we have developed a predictive analytical force, stress and thermal model for machining with worn tools by modifying Oxley’s machining theory. The proposed model combines a work material based deformation model with a moving band heat source model and predicts stresses and temperature distributions on the tool for tool wear modeling. Secondly, we have developed a methodology based on slip-line field analysis and experimental observations to identify tool-chip interface friction in order to investigate the influence of various edge preparations on machining performance. The ideal cutting conditions for a given edge preparation is obtained. Thirdly, we have established physics based models by utilizing 2-D and 3-D finite element methods (FEM) to analyze machining with advanced cutting tool micro geometry in order to improve the tool design for better tool life. Experimental investigations have shown that the cutting tools with advanced edge geometries perform better than uniform edge geometries. Lastly, we have introduced a multi-objective optimization methodology to select optimum machining parameters in the presence of conflicting objectives to assist in decision-making for process planning.
NotePh.D.
NoteIncludes bibliographical references (p. 226-236).
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3HM58VC
LanguageEnglish
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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