Application of Poisson’s ratio structures and decoupling algorithm for 3D force sensing

Abstract

Abstract Flexible tactile electronic devices are extensively used in the fields of robotics, medical detection, and human-computer interaction. Monitoring contact parameters, including force magnitude, direction, and contact location, is particularly vital for skin-like tactile sensing devices. Herein, a 3D force sensor is designed based on porous structure with deliberately designed Poisson’s ratios. A genetic algorithm (GA) optimized back propagation neuronal network (BPNN) model is proposed to support the 3D force decoupling, which can greatly improve the decoupling accuracy. The introduction of the GA-BPNN significantly enhances decoupling accuracy compared to the initial neural network. Micro-porous structures with varied Poisson’s ratios are embedded into the sensing unit to achieve better sensibility. Significantly, this study underscores that the decoupling accuracy of the force components along the Z-axis can be further improved by substituting the solid unit with a designed porous structure unit featuring a specific Poisson’s ratio in an arrayed 3D force sensor.