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Vol.2 , No. 5, Publication Date: Jul. 29, 2015, Page: 220-231
 was adopted to establish the relationship model between the surface roughness and the parameters using Minitab 16. Then, based on analysis of variance (ANOVA), the sensitivities of the surface roughness to the parameters were analyzed. The results show that cutter flutes has high significant influence on surface roughness followed by feed rate and depth of cut, while cutting speed has less significant influence. Finally, the parameters were optimized according to desired surface roughness, and the optimization error (residual) has limited values between -0.02 and 0.02µm.&picture=http://www.aascit.org/aascit/decorators/img/journal/902.jpg)
| [1] | Elssawi Yahya, School of Mechanical Engineering, Advanced Design and Manufacturing Institute, Southwest Jiaotong University, Chengdu, China; School of Mechanical Engineering, Sudan University of Science and Technology, Sudan, Africa. |
| [2] | Guo Fu Ding, School of Mechanical Engineering, Advanced Design and Manufacturing Institute, Southwest Jiaotong University, Chengdu, China. |
| [3] | Sheng Feng Qin, School of Design and Engineering, North Umbria University, London, UK. |
Surface roughness is strongly affected by machining parameters. In the past few decades, many researchers have established the relationship between the surface roughness and the machining parameters. But less attention has been paid to the sensitivity of the surface roughness to the parameters. In addition, the number of tool flutes was ignored, which affects the vibration period and values of the machining system and consequently influences the surface roughness of the machined parts too. Therefore, this study first-time includes the tool flutes in addition to cutting speed, depth of cut and feed rate as independent input variables. Firstly, a set of machining tests were conducted using AA6061 aluminum alloy as work piece material to provide original data, and Response Surface Model (RSM) was adopted to establish the relationship model between the surface roughness and the parameters using Minitab 16. Then, based on analysis of variance (ANOVA), the sensitivities of the surface roughness to the parameters were analyzed. The results show that cutter flutes has high significant influence on surface roughness followed by feed rate and depth of cut, while cutting speed has less significant influence. Finally, the parameters were optimized according to desired surface roughness, and the optimization error (residual) has limited values between -0.02 and 0.02µm.
Keywords
Machining Parameters, Optimization, Response Surface Method, Analysis of Variance, Surface Roughness
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