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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© EuroJournals Publishing, Inc.
2009
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| Online Access: | http://umpir.ump.edu.my/id/eprint/1468/ http://umpir.ump.edu.my/id/eprint/1468/ http://umpir.ump.edu.my/id/eprint/1468/1/2009_J_EJSR_KKadirgama_M.M.Noor-Jurnal-.pdf |
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ump-14682018-01-25T01:19:49Z http://umpir.ump.edu.my/id/eprint/1468/ Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method K., Kadirgama M. M., Noor M. M., Rahman M. R. M., Rejab TJ Mechanical engineering and machinery 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. © EuroJournals Publishing, Inc. 2009 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1468/1/2009_J_EJSR_KKadirgama_M.M.Noor-Jurnal-.pdf K., Kadirgama and M. M., Noor and M. M., Rahman and M. R. M., Rejab (2009) Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method. European Journal of Scientific Research, 25 (2). pp. 250-256. ISSN ISSN 1450-216X http://www.eurojournals.com/ejsr.htm |
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Digital Repository |
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Local University |
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Universiti Malaysia Pahang |
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UMP Institutional Repository |
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Online Access |
| language |
English |
| topic |
TJ Mechanical engineering and machinery |
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TJ Mechanical engineering and machinery K., Kadirgama M. M., Noor M. M., Rahman M. R. M., Rejab Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method |
| description |
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. |
| format |
Article |
| author |
K., Kadirgama M. M., Noor M. M., Rahman M. R. M., Rejab |
| author_facet |
K., Kadirgama M. M., Noor M. M., Rahman M. R. M., Rejab |
| author_sort |
K., Kadirgama |
| title |
Surface Roughness Prediction Model of 6061-T6 Aluminium
Alloy Machining Using Statistical Method |
| title_short |
Surface Roughness Prediction Model of 6061-T6 Aluminium
Alloy Machining Using Statistical Method |
| title_full |
Surface Roughness Prediction Model of 6061-T6 Aluminium
Alloy Machining Using Statistical Method |
| title_fullStr |
Surface Roughness Prediction Model of 6061-T6 Aluminium
Alloy Machining Using Statistical Method |
| title_full_unstemmed |
Surface Roughness Prediction Model of 6061-T6 Aluminium
Alloy Machining Using Statistical Method |
| title_sort |
surface roughness prediction model of 6061-t6 aluminium
alloy machining using statistical method |
| publisher |
© EuroJournals Publishing, Inc. |
| publishDate |
2009 |
| url |
http://umpir.ump.edu.my/id/eprint/1468/ http://umpir.ump.edu.my/id/eprint/1468/ http://umpir.ump.edu.my/id/eprint/1468/1/2009_J_EJSR_KKadirgama_M.M.Noor-Jurnal-.pdf |
| first_indexed |
2023-09-18T21:54:37Z |
| last_indexed |
2023-09-18T21:54:37Z |
| _version_ |
1777413996327993344 |