In this paper, information on the various aspects of smart materials is compiled in an easy-to-consult format by conducting extensive survey of published articles and including the properties of the materials. The compilation of a comprehensive database on smart materials enables to expedite a material selection process in the design of smart material devices or systems. We show the compiled database in a legible format such as GUI based computer software that determines and simulates what material to use based on properties and performance. Finally, the associated system-level models for selected materials are developed and shown in the compilation.
In this paper, concepts associated with the Preisach model and nonlinear mapping functions (neural networks) are coupled to model the hysteretic behavior of piezoceramic actuators. Preisach concepts are utilized in choosing the initial data points and calculating the final displacements having nonlocal memory. In a traditional Preisach model generalization is typically handled by interpolation functions. These functions can lead to significant errors unless the number of data points is considerably high. In this study the generalization of all first order reversal curves is provided by a single neural network. The goal of this work was to enable real-time implementation and learning with a "limited" number of variables. Finally, a novel on-line training approach was developed to account for errors caused by frequency dependency and large variations of the input of the actuator. Results show excellent agreement between simulated and experimental results.