Optimization of Running Blade Prosthetics Utilizing Crow Search Algorithm Assisted by Artificial Neural Networks

A crow search algorithm (CSA) was applied to perform the optimization of a running blade prosthetics (RBP) made composite materials like carbon fibre layers and cores acrylonitrile butadiene styrene (ABS). Optimization aims increase RBP displacement limited by Tsai-Wu failure criterion. Both criterion are predicted using artificial neural networks (ANN) trained with database constructed from finite element method (FEM) simulations. Three different cases optimized varying orientations: –45°/45°, 0°/90°, case two-fibre layer orientations intercalated. Five geometric parameters number selected as design parameters. sensitivity analysis is performed Garzon equation. The best balance between found oriented at 0°/90°. optimal candidate –45°/45° orientation presents higher displacement; however, less than 0.5 not suitable for design. intercalated fibres presented minimal being stiffer damage concentrates mostly in zone that contacts ground. study width were most important