Smart materials have significantly varied properties and their various types are used broadly in many different engineering applications. In order to grow the field and promote its long term viability, it is important to develop tools which enable researchers and practitioners to determine the best smart material for the application. Computerized material selection databases and systems have been recently developed by design and materials engineers to help users select the best materials for an application. However, documentation of smart materials is limited, especially for those aimed at the use of these materials in devices and applications.
In this dissertation, system-level simulation models and collected material data are compiled in a GUI-based computer software called Polymers and Smart Materials Software (PSMS). This material selection tool encompasses material properties and material-level models as well as systems level smart material applications for a wide range of smart materials. This type of compiled data can expedite the material selection process when designing smart material based systems by allowing one to choose the most effective material for the application. The PSMS tool consists of the following three major sections: 1) Polymers (Polymer types and properties, Polymeric behaviors including dielectric and liquid crystal elastomers); 2) Smart Materials (Piezoelectric Ceramics/Polymers, Shape Memory Alloys/Polymers, Thermoelectrics, Electrorheological and Magnetorheological Fluids); 3) More information (External databases, Cost information, etc.).
The software tool offers a wide variety of design and selection features. Material property and performance charts are provided to compare material properties and to choose the best material for optimal performance. The tool is also flexible in that it enables users to categorize material properties and create their own databases. In areas where existing models were inadequate for systems level integration, new models were developed. Towards that end this dissertation highlights the modeling strategies being conducted in the area of liquid crystal elastomers.
The effectiveness and accuracy of the smart material selection tool are evaluated by comparing program outputs with other published experimental results from smart materials and devices. Based on the results, the material and device models are improved. The experimental verification of the material models is presented to show the reliable performance of the smart material design, modeling, and selection tool.