Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artific...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf |
| _version_ | 1796989941788442624 |
|---|---|
| author | M. F. F., Ab Rashid Mohd Rizal, Abdul Lani |
| author_facet | M. F. F., Ab Rashid Mohd Rizal, Abdul Lani |
| author_sort | M. F. F., Ab Rashid |
| collection | UMP |
| description | In CNC milling process, proper setting of cutting
parameter is important to obtain better surface roughness.
Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed
rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method
shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction. |
| first_indexed | 2024-03-06T11:44:28Z |
| format | Conference or Workshop Item |
| id | UMPir5278 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:44:28Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | UMPir52782015-03-03T09:24:02Z http://umpir.ump.edu.my/id/eprint/5278/ Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network M. F. F., Ab Rashid Mohd Rizal, Abdul Lani TS Manufactures In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf M. F. F., Ab Rashid and Mohd Rizal, Abdul Lani (2010) Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network. In: Proceedings of the World Congress on Engineering 2010 , 30 June - 2 July 2010 , London, UK. pp. 2219-2224.. |
| spellingShingle | TS Manufactures M. F. F., Ab Rashid Mohd Rizal, Abdul Lani Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network |
| title | Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
|
| title_full | Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
|
| title_fullStr | Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
|
| title_full_unstemmed | Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
|
| title_short | Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network
|
| title_sort | surface roughness prediction for cnc milling process using artificial neural network |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/5278/1/WCE2010_pp2219-2224.pdf |
| work_keys_str_mv | AT mffabrashid surfaceroughnesspredictionforcncmillingprocessusingartificialneuralnetwork AT mohdrizalabdullani surfaceroughnesspredictionforcncmillingprocessusingartificialneuralnetwork |