Mathematical Modeling to Predict Surface Roughness in CNC Milling
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 p...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
WASET
2009
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF |
| _version_ | 1796989941373206528 |
|---|---|
| author | M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad |
| author_facet | M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad |
| author_sort | M. F. F., Ab Rashid |
| collection | UMP |
| 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.
|
| first_indexed | 2024-03-06T11:44:28Z |
| format | Article |
| id | UMPir5276 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:44:28Z |
| publishDate | 2009 |
| publisher | WASET |
| record_format | dspace |
| spelling | UMPir52762018-02-02T03:09:40Z http://umpir.ump.edu.my/id/eprint/5276/ Mathematical Modeling to Predict Surface Roughness in CNC Milling M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad 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. WASET 2009-05 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF M. F. F., Ab Rashid and Gan, Sin Yi and Noryanti, Muhammad (2009) Mathematical Modeling to Predict Surface Roughness in CNC Milling. Proceedings of World Academy of Science, Engineering and Technology, 3 (5). pp. 393-396. ISSN 2070-3740. (Published) http://waset.org/publications/5381/mathematical-modeling-to-predict-surface-roughness-in-cnc-milling |
| spellingShingle | TJ Mechanical engineering and machinery M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title | Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title_full | Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title_fullStr | Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title_full_unstemmed | Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title_short | Mathematical Modeling to Predict Surface Roughness in CNC Milling |
| title_sort | mathematical modeling to predict surface roughness in cnc milling |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF |
| work_keys_str_mv | AT mffabrashid mathematicalmodelingtopredictsurfaceroughnessincncmilling AT gansinyi mathematicalmodelingtopredictsurfaceroughnessincncmilling AT noryantimuhammad mathematicalmodelingtopredictsurfaceroughnessincncmilling |