Optimization of intelligent prosthetic hands using artificial neural networks and nanoscale technologies for enhanced performance

Annular nano-electromechanical systems (NEMS) in intelligent prosthetic hands enhance precision by serving as highly sensitive sensors for detecting pressure, vibrations, and deformations. This improves feedback and control, enabling users to modulate grip strength and tactile interaction with objects more effectively, enhancing prosthetic functionality. This research focuses on the electro-thermal buckling behavior of multi-directional poroelastic annular NEMS used as temperature sensors in airplanes. In the present study, thermal buckling performance of nano-scale annular functionally graded plate structures integrated with piezoelectric layers under electrical and extreme thermal loadings is investigated. In this regard, piezoelectric layers are placed on a disk made of metal matrix composite with graded properties in three radials, thickness and circumferential directions. The grading properties obey the power-law distribution. The whole structure is embedded in thermal environment. To model the mechanical behavior of the structure, a novel four-variable refined quasi-3D sinusoidal shear deformation theory (RQ-3DSSDT) is engaged in obtaining displacement field in the whole structure. The validity of the results is examined by comparing to a similar problem published in literature. The results of the buckling behavior of the structure in different boundary conditions are presented based on the critical temperature rise and critical external voltage. It is demonstrated that increase in the nonlocal and gradient length scale factor have contradicting effects on the critical temperature rise. On the other hand, increase in the applied external voltage cause increase in the critical temperature. Effects of other parameters like geometrical parameters and grading indices are presented and discussed in details.