Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method
This paper explores on the optimization of the surface roughness of milling mould 6061-T6 aluminium alloys with carbide coated inserts. Optimization of the milling is very important to reduce the cost and time for machining mould. The purposes of this study are to develop the predicting model of sur...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
© EuroJournals Publishing, Inc.
2009
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/1468/1/2009_J_EJSR_KKadirgama_M.M.Noor-Jurnal-.pdf http://umpir.ump.edu.my/id/eprint/1468/ http://www.eurojournals.com/ejsr.htm |
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| Summary: | This paper explores on the optimization of the surface roughness of milling mould 6061-T6 aluminium alloys with carbide coated inserts. Optimization of the milling is very important to reduce the cost and time for machining mould. The purposes of this study are to develop the predicting model of surface roughness, to investigate the most dominant variables among the cutting speed, feed rate, axial depth and radial depth and to optimize
Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method 251 the parameters. Response surface method based optimization approach was used in this study. It can be seen from the first order model that the feed rate is the most significantly influencing factor for the surface roughness. Second-order model reveals that there is no
interaction between the variables and response. The parameters. Response surface method based optimization approach was used in this study. It can be seen from the first order model that the feed rate is the most significantly influencing factor for the surface roughness. Second-order model reveals that there is no
interaction between the variables and response. |
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