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Multi-objective grey correlation analysis based on CFRP Helical Milling simulation model.

Authors :
Zhou, Lan
Wang, Yunlong
An, Guosheng
Zhu, Ruibiao
Li, Guangqi
Ma, Rong
Source :
International Journal of Advanced Manufacturing Technology; Nov2024, Vol. 135 Issue 3/4, p1565-1585, 21p
Publication Year :
2024

Abstract

Helical milling is widely used in aerospace as a key processing technology for Carbon fiber reinforced polymer (CFRP). However, the eccentric machining characteristics lead to an unusually complex pattern of cutting force and residual stress distribution on the work-piece during helical milling processing. Based on the Hashin failure criterion, a 3D FEM model of CFRP helical milling was built for analyzing the changing law of cutting force, then the three factors and three levels orthogonal tests were used to investigate the influence of machining parameters on the axial force, radial force, and minimum principal residual stress, finally, the multi-objective optimization based on grey correlation analysis was realized. The results showed that the errors of axial force and radial force obtained by simulation and experiment were 10.68% and 12.26%, respectively. The axial force and radial force were negatively correlated to the spindle speed, positively correlated to the axial cutting depth, and uncorrelated to the feed per tooth. The minimum principal residual stress was negatively correlated to the spindle speed, positively correlated to the feed per tooth, and uncorrelated to the axial cutting depth. The degree of influence on optimization of machining parameters was: spindle speed > axial cutting depth > feed per tooth. The corresponding average grey correlation degree differences were 0.280981, 0.216859, and 0.013422, respectively. The maximum value of grey correlation degree in the orthogonal test was 0.874372, and the corresponding optimal parameters combination was the spindle speed 8000 r/min, feed per tooth 0.03 mm/z, and axial cutting depth 0.2 mm/r. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02683768
Volume :
135
Issue :
3/4
Database :
Complementary Index
Journal :
International Journal of Advanced Manufacturing Technology
Publication Type :
Academic Journal
Accession number :
180373781
Full Text :
https://doi.org/10.1007/s00170-024-14419-5