Mathematical modeling to predict surface roughness in milling process.
Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness pr...
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ump-236 |
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eprints |
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ump-2362017-04-06T08:10:28Z http://umpir.ump.edu.my/id/eprint/236/ Mathematical modeling to predict surface roughness in milling process. Gan, Sin Yi TJ Mechanical engineering and machinery Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set. 2008-11 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/236/1/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Table%20of%20content%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/236/2/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Abstract%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/236/3/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Chapter%201%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/236/4/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28References%29.pdf Gan, Sin Yi (2008) Mathematical modeling to predict surface roughness in milling process. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:40641&theme=UMP2 |
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Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
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UMP Institutional Repository |
| collection |
Online Access |
| language |
English English English English |
| topic |
TJ Mechanical engineering and machinery |
| spellingShingle |
TJ Mechanical engineering and machinery Gan, Sin Yi Mathematical modeling to predict surface roughness in milling process. |
| description |
Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set. |
| format |
Undergraduates Project Papers |
| author |
Gan, Sin Yi |
| author_facet |
Gan, Sin Yi |
| author_sort |
Gan, Sin Yi |
| title |
Mathematical modeling to predict surface roughness in milling process. |
| title_short |
Mathematical modeling to predict surface roughness in milling process. |
| title_full |
Mathematical modeling to predict surface roughness in milling process. |
| title_fullStr |
Mathematical modeling to predict surface roughness in milling process. |
| title_full_unstemmed |
Mathematical modeling to predict surface roughness in milling process. |
| title_sort |
mathematical modeling to predict surface roughness in milling process. |
| publishDate |
2008 |
| url |
http://umpir.ump.edu.my/id/eprint/236/ http://umpir.ump.edu.my/id/eprint/236/ http://umpir.ump.edu.my/id/eprint/236/1/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Table%20of%20content%29.pdf http://umpir.ump.edu.my/id/eprint/236/2/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Abstract%29.pdf http://umpir.ump.edu.my/id/eprint/236/3/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28Chapter%201%29.pdf http://umpir.ump.edu.my/id/eprint/236/4/Mathematical%20modeling%20to%20predict%20surface%20roughness%20in%20milling%20process%20%28References%29.pdf |
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2023-09-18T21:52:12Z |
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2023-09-18T21:52:12Z |
| _version_ |
1777413844220510208 |