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 class="fixed-width table-group" id="tab11"><tr><td><table class="table"><colgroup><col style="width:13.20em"/><col style="width:4.34em"/><col style="width:4.91em"/></colgroup><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Performances </td><td class="align_center">MOGA</td><td class="align_center">NSGA-II</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">Equivalent stress (MPa)</td><td class="align_center">140.65</td><td class="align_center">140.82</td></tr><tr><td class="align_left">Displacement (<i>μ</i>m)</td><td class="align_center">398.5</td><td class="align_center">397.4</td></tr><tr><td class="align_left">Safety factor</td><td class="align_center">3.60</td><td class="align_center">3.57</td></tr><tr><td class="align_left">Computational time (minute)</td><td class="align_center">9.7</td><td class="align_center">10.5</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of the optimal performances obtained from MOGA an NSGA-II.</div>

Mathematical Problems in Engineering

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Table 11

Table 11: 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 11 | 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 