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

Table 5

ANOVA for response 2: channel 2 (worktable vibration).

Source	Sum of squares	df	Mean square	F-value	value
Model	4.778E − 06	20	2.389E − 07	53.04	<0.0001	Significant
N	2.204E − 07	1	2.204E − 07	48.94	<0.0001
	2.042E − 08	1	2.042E − 08	4.53	0.0567
	1.838E − 07	1	1.838E − 07	40.80	<0.0001
	3.375E − 08	1	3.375E − 08	7.49	0.0193
γ	1.504E − 07	1	1.504E − 07	33.40	0.0001
N	2.256E − 07	1	2.256E − 07	50.09	<0.0001
N	3.306E − 07	1	3.306E − 07	73.40	<0.0001
N	5.625E − 09	1	5.625E − 09	1.25	0.2876
N γ	1.563E − 08	1	1.563E − 08	3.47	0.0894
	3.063E − 08	1	3.063E − 08	6.80	0.0244
	1.563E − 08	1	1.563E − 08	3.47	0.0894
γ	1.406E − 07	1	1.406E − 07	31.22	0.0002
	5.256E − 07	1	5.256E − 07	116.70	<0.0001
γ	3.906E − 07	1	3.906E − 07	86.73	<0.0001
γ	1.806E − 07	1	1.806E − 07	40.10	<0.0001
	1.339E − 06	1	1.339E − 06	297.24	<0.0001
	2.867E − 07	1	2.867E − 07	63.65	<0.0001
	4.667E − 07	1	4.667E − 07	103.62	<0.0001
	1.700E − 07	1	1.700E − 07	37.75	<0.0001
γ ^2	1.188E − 07	1	1.188E − 07	26.37	0.0003
Residual	4.955E − 08	11	4.504E − 09
Lack of fit	2.121E − 08	6	3.535E − 09	0.6239	0.7103	Not significant
Pure error	2.833E − 08	5	5.667E − 09
Cor total	4.827E − 06	31