Predictive modeling and multi-objective optimization of maching-induced residual stresses
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Ulutan, Durul.
Predictive modeling and multi-objective optimization of maching-induced residual stresses. Retrieved from
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TitlePredictive modeling and multi-objective optimization of maching-induced residual stresses
Date Created2013
Other Date2013-01 (degree)
Extentxxv, 216 p. : ill.
DescriptionIn the aerospace industry, titanium and nickel-based alloys are frequently used for critical structural components, especially due to their higher strength at both low and high temperatures, and higher wear and chemical degradation resistance. However, because of their unfavorable thermal properties, deformation and friction-induced microstructural changes prevent the end products from having good surface integrity properties. In addition to surface roughness, microhardness changes, and microstructural alterations, the machining-induced residual stress profiles of titanium and nickel-based alloys contribute in the surface integrity of these products. Therefore, it is essential to create a comprehensive method that predicts the residual stress outcomes of machining processes, and understand how machining parameters (cutting speed, uncut chip thickness, depth of cut, etc.) or tool parameters (tool rake angle, cutting edge radius, tool material/coating, etc.) affect the machining-induced residual stresses. Since experiments involve a certain amount of error in measurements, physics-based simulation experiments should also involve an uncertainty in the predicted values, and a rich set of simulation experiments are utilized to create expected value and variance for predictions. As the first part of this research, a method to determine the friction coefficients during machining from practical experiments was introduced. Using these friction coefficients, finite element-based simulation experiments were utilized to determine flow stress characteristics of materials and then to predict the machining-induced forces and residual stresses, and the results were validated using the experimental findings. A sensitivity analysis on the numerical parameters was conducted to understand the effect of changing physical and numerical parameters, increasing the confidence on the selected parameters, and the effect of machining parameters on machining-induced forces and residual stresses was also investigated. Finally, these predictions were inputted to a multi-objective optimization methodology utilizing Particle Swarm Optimization algorithm to select the optimal machining parameters where competing or conflicting objectives constitute hurdles in the decision-making process of manufacturing plans in the industrial applications.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Durul Ulutan
Genretheses, ETD doctoral
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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