Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties

Conductive silicone rubber has great advantages for tactile sensing applications. The electrical behavior of the elastomeric material is rate-dependent and exhibit hysteresis upon cyclic loading. Several constitutive models were developed for mechanical simulation of this material upon loading and unloading. One of the successful approaches to model the time-dependent behavior of elastomers is Bergstrom–Boyce model. An adaptive neuro-fuzzy inference system (ANFIS) model will be established in this study to predict the stress–strain changing of conductive silicone rubber during compression tests. Various compression tests were performed on the produced specimens. An ANFIS is used to approximate correlation between measured features of the material and to predict its unknown future behavior for stress changing. ANFIS has unlimited approximation power to match any nonlinear functions well and to predict a chaotic time series.