An Optimized Design of New XYθ Mobile Positioning Microrobotic Platform for Polishing Robot Application Using Artificial Neural Network and Teaching-Learning Based Optimization

Table 7

Analysis of variance for safety factor.

Source	DF	Seq SS	Contribution (%)	Adj SS	Adj MS	F-value	value
Model	20	0.396108	72.62	0.396108	0.019805	0.80	0.679
Linear	5	0.210502	38.59	0.167944	0.033589	1.35	0.359
A	1	0.031505	5.78	0.041489	0.041489	1.67	0.244
B	1	0.010822	1.98	0.003362	0.003362	0.14	0.726
C	1	0.160082	29.35	0.109272	0.109272	4.39	0.081
D	1	0.000303	0.06	0.001510	0.001510	0.06	0.814
E	1	0.007789	1.43	0.010727	0.010727	0.43	0.536
Square	5	0.020878	3.83	0.020510	0.004102	0.16	0.967
A^∗A	1	0.006593	1.21	0.001006	0.001006	0.04	0.847
B^∗B	1	0.000844	0.15	0.000710	0.000710	0.03	0.871
C^∗C	1	0.010358	1.90	0.012192	0.012192	0.49	0.510
D^∗D	1	0.000886	0.16	0.002319	0.002319	0.09	0.770
E^∗E	1	0.002196	0.40	0.002143	0.002143	0.09	0.779
2-Way interaction	10	0.164728	30.20	0.164728	0.016473	0.66	0.731
A^∗B	1	0.001177	0.22	0.001342	0.001342	0.05	0.824
A^∗C	1	0.056306	10.32	0.056556	0.056556	2.27	0.182
A^∗D	1	0.000000	0.00	0.000038	0.000038	0.00	0.970
A^∗E	1	0.000713	0.13	0.001278	0.001278	0.05	0.828
B^∗C	1	0.004343	0.80	0.003475	0.003475	0.14	0.721
B^∗D	1	0.018617	3.41	0.018649	0.018649	0.75	0.420
B^∗E	1	0.057861	10.61	0.060386	0.060386	2.43	0.170
C^∗D	1	0.000085	0.02	0.000084	0.000084	0.00	0.955
C^∗E	1	0.022801	4.18	0.023640	0.023640	0.95	0.367
D^∗E	1	0.002825	0.52	0.002825	0.002825	0.11	0.748
Error	6	0.149333	27.38	0.149333	0.024889
Total	26	0.545441	100.00