Tool Life Analysis By Partial Swarm Optimisation
Tool life is one of the main factors to be considered in CNC milling machine. Prediction model and optimum values are very important for the machinist to save number of cutting tools and reduce machining time. The aim of the this paper is to develop the tool life prediction model for P20 tool ste...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/1449/ http://umpir.ump.edu.my/id/eprint/1449/1/2009_P_NAE09_M.M.Noor_K.Kadirgama-conference-.pdf |
| Summary: | Tool life is one of the main factors to be considered in CNC milling machine. Prediction
model and optimum values are very important for the machinist to save number of cutting
tools and reduce machining time. The aim of the this paper is to develop the tool life
prediction model for P20 tool steel with aid of statistical method and to determine the
optimisation values using partial swarm optimisation (PSO) for coated carbide cutting tool
under different cutting conditions. By using response surface method, first and second
order models were developed with 95% confidence level. The tool life model was
developed in terms of cutting speed, feed rate, axial depth and radial depth. In general, the
results obtained from the mathematical model are in good agreement with that obtained
from the experiment data’s. It was found that the feed rate, cutting speed, axial depth and
radial depth played a major role to determine the tool life. On the other hand, the tool life
increases with the reduction of cutting speed and feed rate. For end-milling of P20 tool
steel, the optimum cutting speed, feed rate, axial depth and radial depth obtained from PSO
are of 100 m/s, 0.1 mm/rev, 1.9596 mm and 2 mm respectively. The optimized tool life of
40.52 min was obtained using the above mentioned parameters. |
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