An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method
Surface roughness is one of the most important requirements in machining process. In order to obtain needed surface roughness, the proper setting of cutting parameters is crucial before the process take place. Therefore, an accurate mathematical model to predict surface roughness is totally needed....
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| Main Author: | |
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| Format: | Article |
| Language: | English English |
| Published: |
IACSIT Press
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6693/2/162-S103_Improved_Math_Model.pdf http://umpir.ump.edu.my/id/eprint/6693/3/fkm-2014-fadzil-An_Improved_Mathematical.pdf http://umpir.ump.edu.my/id/eprint/6693/ http://www.ijmmm.org/index.php?m=content&c=index&a=show&catid=34&id=201 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English English |
| Summary: | Surface roughness is one of the most important requirements in machining process. In order to obtain needed surface roughness, the proper setting of cutting parameters is crucial before the process take place. Therefore, an accurate mathematical model to predict surface roughness is totally needed. This research presents a hybrid method which combine conventional multiple regression analysis and genetic algorithm to improve the accuracy of mathematical model to predict surface roughness. In experiment, three independent variables: spindle speed, feed rate and depth of cut were manipulated in collecting data. Full factorials cut were performed using FANUC CNC Milling α-Τ14ιE. The results show that the proposed hybrid method capable to improve accuracy of model with 23% and 28% of reduction in error. |
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