Selection of hyper-elastic materials and pattern of differentiated soft pads emulating human finger compliance in robotic fingertips

Purpose The purpose of this study is to enhance the grasping reliability of robotic hands by examining how fingertip pad compliance is influenced by material selection, pattern geometry and compressive load. Human fingertips exhibit unique compliance characteristics, which this research aims to emulate through engineered soft pads. By determining how design parameters affect pad flattening, the study aims to optimize soft pad interfaces for more secure and adaptive robotic grasping. The ultimate goal is to identify material–geometry combinations that maximize compliance, enabling robotic fingertips to conform better to objects during manipulation tasks. Design/methodology/approach This study investigates the flattening behavior of soft fingertip pads for robotic hands by varying pad patterns, compressive load, and hyperelastic material type. Three pad patterns were fabricated using two hyperelastic materials – Tango Gray™ and Tango Plus™. A Taguchi L9 orthogonal array was used to evaluate nine combinations of these design parameters under compressive loading against a rigid surface. Pad flattening was recorded for each configuration. Statistical methods identified key parameters, and a modified Taguchi approach was employed to establish an empirical relationship between input variables and pad compliance, supporting performance optimization of robotic fingertip interfaces. Findings The study found that compressive load had the highest influence on pad flattening, contributing 55.4% for Tango Gray™ and 88.1% for Tango Plus™. Among the tested patterns and materials, intricate pad geometries combined with softer materials resulted in greater flattening, indicating enhanced compliance. The modified Taguchi method successfully developed empirical models linking design parameters to performance outcomes. These results help pinpoint the optimal combinations of geometry and material that improve fingertip pad compliance, which is vital for improving contact area and grasp stability in robotic hands during object manipulation. Originality/value This work uniquely combines differentiated pad geometries, hyper-elastic materials and statistical design of experiments to mimic human fingertip compliance in robotic applications. Unlike conventional robotic fingertips, which often lack adaptive surface behavior, the developed pads offer tunable compliance through pattern and material selection. The use of a modified Taguchi approach provides an efficient way to model and optimize complex interactions among design variables. The ability to fabricate soft pads with varying stiffness and intricate features using advanced materials and techniques adds significant value to the development of functional, adaptive robotic grasping systems.