Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td align="left">Materials</td><td align="center">Error in %</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td align="left">AR66 Al-alloy</td><td align="center">4.20</td></tr><tr><td align="left">Chromium steel (H11)</td><td align="center">0.40</td></tr><tr><td align="left">Low carbon steel (LC)</td><td align="center">3.90</td></tr><tr><td align="left">Medium carbon steel (MC)</td><td align="center">1.70</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

 Error in neural network predicted yield strength.

Advances in Artificial Neural Systems

tab4

Table 4

Table 4: Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks 