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="tab14"><tr><td><table class="table"><colgroup><col style="width:9.09em"/><col style="width:6.53em"/><col style="width:7.49em"/><col style="width:8.42em"/></colgroup><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Responses</td><td class="align_center">Initial design</td><td class="align_center">Optimal design</td><td class="align_center">Improvement (%)</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">Displacement (<i>μ</i>m)</td><td class="align_center">295.59</td><td class="align_center">330.68</td><td class="align_center">11.87</td></tr><tr><td class="align_left">Safety factor</td><td class="align_center">2.7331</td><td class="align_center">3.5952</td><td class="align_center">31.54</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of initial design with optimal design.</div>

Mathematical Problems in Engineering

tab14

Table 14

Table 14: 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 14 | 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 