Mathematical Problems in Engineering / 2018 / Article / Tab 6 / Research Article
An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester Table 6 Response surface regression of displacement versus design parameters.
Source DF Seq SS Contribution Adj SS Adj MS F-Value P-Value Model 35 143821 99.61% 143821 4109 316.88 0.000 Linear 7 142517 98.71% 110807 15830 1220.71 0.000 l 1 1 548 0.38% 436 436 33.63 0.000 t 1 1 17 0.01% 39 39 2.99 0.091 l 2 1 141172 97.78% 109802 109802 8467.46 0.000 t 2 1 657 0.46% 454 454 34.99 0.000 l 3 1 102 0.07% 56 56 4.35 0.043 t 3 1 14 0.01% 12 12 0.94 0.339 1 7 0.01% 3 3 0.23 0.635 Square 7 1064 0.74% 1061 152 11.69 0.000 l 1 l 1 1 42 0.03% 6 6 0.44 0.511 t 1 t 1 1 74 0.05% 9 9 0.70 0.409 l 2 l 2 1 924 0.64% 421 421 32.44 0.000 t 2 t 2 1 2 0.00% 0 0 0.00 0.997 l 3 l 3 1 11 0.01% 8 8 0.65 0.424 t 3 t 3 1 11 0.01% 5 5 0.36 0.551 1 0 0.00% 0 0 0.04 0.849 2-Way Interaction 21 240 0.17% 240 11 0.88 0.614 l 1 t1 1 4 0.00% 5 5 0.35 0.555 l 1 l 2 1 0 0.00% 0 0 0.02 0.902 l 1 t 2 1 1 0.00% 1 1 0.11 0.745 l 1 l 3 1 16 0.01% 16 16 1.21 0.277 l 1 t 3 1 1 0.00% 1 1 0.10 0.748 l 1 1 2 0.00% 2 2 0.17 0.682 t 1 l 2 1 38 0.03% 33 33 2.54 0.118 t 1 t 2 1 3 0.00% 3 3 0.23 0.635 t 1 l 3 1 0 0.00% 1 1 0.05 0.820 t 1 t 3 1 1 0.00% 1 1 0.07 0.795 t 1 1 0 0.00% 0 0 0.00 0.990 l 2 t 2 1 139 0.10% 138 138 10.63 0.002 l 2 l 3 1 5 0.00% 5 5 0.39 0.534 l 2 t 3 1 16 0.01% 16 16 1.26 0.269 l 2 1 1 0.00% 1 1 0.04 0.837 t 2 l 3 1 5 0.00% 5 5 0.42 0.520 t 2 t 3 1 0 0.00% 0 0 0.00 0.964 t 2 1 3 0.00% 2 2 0.19 0.663 l 3 t 3 1 1 0.00% 1 1 0.11 0.743 l 3 1 2 0.00% 2 2 0.13 0.722 t 3 1 0 0.00% 0 0 0.03 0.855 Error 43 558 0.39% 558 13 Total 78 144379 100.00%
