TY - JOUR
T1 - Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine
AU - Zhao, Tao
AU - Shi, Yaoyao
AU - Lin, Xiaojun
AU - Duan, Jihao
AU - Sun, Pengcheng
AU - Zhang, Jun
N1 - Publisher Copyright:
© 2014, Springer-Verlag London.
PY - 2014/6/8
Y1 - 2014/6/8
N2 - In order to improve surface quality and reduce surface roughness of integrally bladed rotors (IBRs), the optimizations of grinding and polishing process parameters, such as abrasive size, contact force, belt linear velocity, and feed rate, are carried out in this study. Firstly, the optimal range of each factor is obtained by single-factor experiment, and a surface roughness prediction model is generated according to the central composite design experiments and quadratic regression design technology. Secondly, the optimal level of each factor is confirmed through the surface roughness prediction model, and the response surface of the optimal level of each factor is drawn using Matlab. Then, the optimum process parameters are determined by analyzing the response surface. Finally, the optimum process parameters are selected in the grinding and polishing experiments for IBR. The experimental results show that grinding and polishing with the optimum parameters obtained in this study enhances the machining quality significantly. The surface roughness of IBR improved greatly (nearly 25%) compared with grinding using normal parameters.
AB - In order to improve surface quality and reduce surface roughness of integrally bladed rotors (IBRs), the optimizations of grinding and polishing process parameters, such as abrasive size, contact force, belt linear velocity, and feed rate, are carried out in this study. Firstly, the optimal range of each factor is obtained by single-factor experiment, and a surface roughness prediction model is generated according to the central composite design experiments and quadratic regression design technology. Secondly, the optimal level of each factor is confirmed through the surface roughness prediction model, and the response surface of the optimal level of each factor is drawn using Matlab. Then, the optimum process parameters are determined by analyzing the response surface. Finally, the optimum process parameters are selected in the grinding and polishing experiments for IBR. The experimental results show that grinding and polishing with the optimum parameters obtained in this study enhances the machining quality significantly. The surface roughness of IBR improved greatly (nearly 25%) compared with grinding using normal parameters.
KW - Grinding and polishing process
KW - IBR
KW - Surface roughness
KW - The response surface method
UR - http://www.scopus.com/inward/record.url?scp=84920707252&partnerID=8YFLogxK
U2 - 10.1007/s00170-014-6020-3
DO - 10.1007/s00170-014-6020-3
M3 - 文章
AN - SCOPUS:84920707252
SN - 0268-3768
VL - 74
SP - 653
EP - 663
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 5-8
ER -