Optimization of tool life using in milling using radial basis function network
This paper discuss of the Optimization of tool life in milling using Radial basis Function Network (RBFN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using high speed steel coated HS-Co as the cutting tool and aluminium alloy T6061 as m...
| Main Author: | |
|---|---|
| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/ http://umpir.ump.edu.my/id/eprint/1459/1/Mohd_Faizal_Aziz_%28_CD_5048_%29.pdf |
| Summary: | This paper discuss of the Optimization of tool life in milling using Radial basis Function Network (RBFN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using high speed steel coated HS-Co as the cutting tool and aluminium alloy T6061 as material due to predict the resulting of flank wear. Data is collected from RoboDrill T14i CNC milling machines were run by 15 samples of experiments using DOE approach that generate by Box-Behnkin method due to table design in MINITAB packages. The inputs of the model consist of feed, cutting speed and depth of cut while the output from the model is Flank wear occur on the tool surface. The model is validated through a comparison of the experimental values with their predicted counterparts. The analysis of the flank wear is using IM1700 Inverted Metallograph microscope for examine the minimum size of the flank wear within 0.3mm. The optimization of the tool life is studied to compare the relationship of the parameters involve. Cutting speed is the greater influence to the tool fatigue criterion which is result the performance of the cutting tool. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining. |
|---|