Grinding is an abrasive machining process used for the final shaping of components that require very smooth surfaces and a high dimensional accuracy. In recent years, the costs of industrial grinding operations have increased with a greater demand for high-strength, low-weight superalloy components. Titanium and nickel-based alloys are desirable for their high creep-rupture strength and corrosion and oxidation resistance in high-temperature environments. However, they are very difficult to grinding due to a combination of poor thermal properties, rapid work-hardening, and a high level of chemical reactivity. In this thesis, two methods are investigated to improve process performance in plunge centerless grinding of Inconel 718 and Ti-6Al-4V superalloy fasteners: (i) economic optimization of grinding process parameters and (ii) reduced quantity lubrication using a graphite nanoplatelet-enhanced grinding fluid. In the first part, a systematic methodology is presented for finding the optimum parameters in two stages: (i) modeling of process and part quality constraints, and (ii) determination of optimum grinding conditions in the feasible operating region. In the second part, the performance of a graphite nanoplatelet-enhanced grinding fluid in reduced quantity lubrication centerless grinding is evaluated to assess its potential as a cost-effective alternative to the traditional flood cooling method. The results of the study indicate that an appreciable reduction in the cost of the superalloy grinding operation can be achieved by operating at the optimum parameters. In addition, it is shown that the application of a graphite-enhanced fluid at a reduced flow rate is more effective than high-volume flood cooling in reducing specific grinding energy levels and wheel wear rates, thus offering the potential to increase process productivity.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/37113 |
Date | 17 November 2009 |
Creators | Dzebo, Sead |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Detected Language | English |
Type | Thesis |
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