Mohammad Rizal, Abdul Lani (2009) Modeling of milling process to predict surface roughness using artificial intelligent method. Faculty of Mechanical Engineering, Universiti Malaysia Pahang.
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Abstract
This thesis presents the milling process modeling to predict surface roughness. 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.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009, SV: MR MOHD FADZIL FAISAE BIN AB RASHID, NO. CD: 4187 |
| Uncontrolled Keywords: | Surface roughness Machining Artificial intelligence |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Faculty of Mechanical Engineering |
| Depositing User: | Rosfadilla Mohamad Zainun |
| Date Deposited: | 16 Aug 2010 03:29 |
| Last Modified: | 28 Apr 2023 07:44 |
| URI: | http://umpir.ump.edu.my/id/eprint/800 |
| Download Statistic: | View Download Statistics |
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