TY - JOUR
T1 - An investigation of surface roughness in micro-end-milling of metals
AU - Jing, Xiubing
AU - Li, Huaizhong
AU - Wang, Jun
AU - Yuan, Yanjie
AU - Zhang, Dawei
AU - Kwok, Ngaiming
AU - Nguyen, Thai
PY - 2017
Y1 - 2017
N2 - This paper presents an experimental study of the effects of cutting parameters of micro-end-milling process on the machined surface roughness, in order to find the optimal operation conditions for improved surface finish. Three types of metals, namely 6160 aluminium alloy, brass, and AISI 1040 steel, are used as work materials. The effect of material property on the surface roughness is investigated. It is found that under the same machining condition, the machined surface quality of aluminium alloy is the worst, while that of brass is the best. A multiple regression model for the surface roughness is developed, which includes the effects of cutting speed, feedrate, and the interaction between them. Results based on analysis of variance (ANOVA) show that the cutting speed is the most significant factor on surface roughness. The residual analysis indicates that the multiple regression model is valid and agrees with the experimental results.
AB - This paper presents an experimental study of the effects of cutting parameters of micro-end-milling process on the machined surface roughness, in order to find the optimal operation conditions for improved surface finish. Three types of metals, namely 6160 aluminium alloy, brass, and AISI 1040 steel, are used as work materials. The effect of material property on the surface roughness is investigated. It is found that under the same machining condition, the machined surface quality of aluminium alloy is the worst, while that of brass is the best. A multiple regression model for the surface roughness is developed, which includes the effects of cutting speed, feedrate, and the interaction between them. Results based on analysis of variance (ANOVA) show that the cutting speed is the most significant factor on surface roughness. The residual analysis indicates that the multiple regression model is valid and agrees with the experimental results.
UR - https://hdl.handle.net/1959.7/uws:64351
U2 - 10.1080/14484846.2016.1211472
DO - 10.1080/14484846.2016.1211472
M3 - Article
SN - 1448-4846
VL - 15
SP - 166
EP - 174
JO - Australian Journal of Mechanical Engineering
JF - Australian Journal of Mechanical Engineering
IS - 3
ER -