A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process

Table 4

ANOVA results of response 1: channel 1 (spindle vibration).

Source	Sum of squares	Df	Mean square	F-value	value
Model	0.0003	20	0.0000	37.13	<0.0001	Significant
N	0.000024	1	0.0000	67.02	<0.0001
	1.667E − 07	1	1.667E − 07	0.4654	0.5092
	6.000E − 06	1	6.000E − 06	16.75	0.0018
	0.0000	1	0.0000	37.70	<0.0001
γ	6.667E − 07	1	6.667E − 07	1.86	0.1997
N	1.000E − 06	1	1.000E − 06	2.79	0.1229
N	2.500E − 07	1	2.500E − 07	0.6981	0.4212
N	9.000E − 06	1	9.000E − 06	25.13	0.0004
N γ	9.000E − 06	1	9.000E − 06	25.13	0.0004
	1.000E − 06	1	1.000E − 06	2.79	0.1229
	2.500E − 07	1	2.500E − 07	0.6981	0.4212
γ	2.250E − 06	1	2.250E − 06	6.28	0.0292
	1.000E − 06	1	1.000E − 06	2.79	0.1229
γ	1.000E − 06	1	1.000E − 06	2.79	0.1229
γ	2.500E − 07	1	2.500E − 07	0.6981	0.4212
	0.0001	1	0.0001	385.86	<0.0001
	8.523E − 07	1	8.523E − 07	2.38	0.1512
	0.0000	1	0.0000	69.40	<0.0001
	0.0001	1	0.0001	165.26	<0.0001
	2.561E − 06	1	2.561E − 06	7.15	0.0216
Residual	3.939E − 06	11	3.581E − 07
Lack of fit (LF)	3.106E − 06	6	5.177E − 07	3.11	0.1171	Not significant
Error	8.333E − 07	5	1.667E − 07
Cor total	0.0003	31