Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm
In this study, statistical models were developed using the capabilities of Response Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti- 6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine with a high speed att...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Trans Tech Publications, Switzerland
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/3109/ http://irep.iium.edu.my/3109/ http://irep.iium.edu.my/3109/ http://irep.iium.edu.my/3109/1/Predicting_and_investigating_surface_response.pdf |
| Summary: | In this study, statistical models were developed using the capabilities of Response
Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti-
6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine
with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining
variables. The adequacy of the model was tested at 95% confidence interval. Meanwhile, a time
trend was observed in residual values between model predictions and experimental data, reflecting
little deviations in surface roughness prediction. A very good performance of the RSM model, in
terms of agreement with experimental data, was achieved. It is observed that cutting speed has the most significant influence on surface roughness followed by feed and depth of cut. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the surface roughness in flat end milling of Ti-6Al-4V materials. The developed quadratic
prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness. |
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