Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/3924
Title: MACHINABILITY STUDIES AND FEM MODELING OF TITANIUM ALLOY MACHINING
Keywords: MECHANICAL & INDUSTRIAL ENGINEERING;FEM MODELING;TITANIUM ALLOY MACHINING;TITANIUM
Issue Date: 2012
Abstract: Titanium is classified as a difficult-to-machine material due to its peculiar characteristics such as poor thermal conductivity, high- strength at elevated temperature, resistance to wear and chemical degradation. Even though a new generation of machining techniques and tool materials are now available which are revolutionizing machinability of a large number of work materials, the machining of titanium continues to be a challenging task. Rapid tool wear in machining of titanium alloys is the main problem due to high cutting zone temperature localised in the vicinity of the cutting edge and enhanced chemical reactivity of titanium with the tool material. It has been shown that insert geometry plays a significant role in achieving good performance, high productivity and low surface roughness during machining. In the present investigation which is divided into two phases the effect of different insert geometries namely rhomboid with 95° approach angles, square and round, on the rate of flank wear, main cutting force and surface roughness during machining of titanium alloy has been studied under dry cutting conditions during phase I. A substantial improvement in tool life and surface finish was obtained for the round shaped insert. Cutting temperature for different insert geometries were also theoretically estimated by Finite element method. The results obtained provide a fundamental understanding on how tool geometry affects the tool temperature during machining. In phase II, the insert geometry which performed the best in phase I study i.e round shaped insert was selected for further parameter optimization study using Taguchi's method. The statistical methods of signal to noise ratio (S/N ratio) and the analysis of variance (ANOVA) were used to optimize the cutting parameters and rank the contributing factors which influence the process.
URI: http://hdl.handle.net/123456789/3924
Other Identifiers: M.Tech
Research Supervisor/ Guide: Jain, P. K.
Mehta, N. K.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (MIED)

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