Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
The selection of optimal cutting parameters has always presented a critical quality concern in the micromachining process. This study examines the effects of three process parameters which are spindle speed, feed rate and depth of cut on the process outputs. The outputs are the surface area roughne...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/47576/1/FK%202013%2049R.pdf |
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| Summary: | The selection of optimal cutting parameters has always presented a critical quality concern in the micromachining process. This study examines the effects of three
process parameters which are spindle speed, feed rate and depth of cut on the process outputs. The outputs are the surface area roughness and burr formation in micro-end
milling of Ti-6Al-4V titanium alloy. Response surface methodology was utilized to develop mathematical models of the process outputs. In addition, analysis of variance
and confirmation runs were employed to verify the precision of the mathematical models. Finally, non-dominated sorting genetic algorithm-II as evolutionary optimization approach was used for multi-objective optimization of the micro-end
milling process. The optimization results demonstrate the high performance of this method to obtain the Pareto optimal set of solutions in the micro-end milling process.
With the optimal parameter sets, an operator can select a suitable combination of variables to obtain a better surface finish or lower burr formation. Optimal machining
parameters were the spindle speed of 40000 rpm, the feed rate of 61-75 mm/min, and the depth of cut of 86-92 μm.
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