Devin Roach1,Jeremy Herman1,2,Samuel Leguizamon1,Erik Linde1,Adam Cook1,Timothy White2,Bryan Kaehr1
Sandia National Laboratories1,University of Colorado Boulder2
Devin Roach1,Jeremy Herman1,2,Samuel Leguizamon1,Erik Linde1,Adam Cook1,Timothy White2,Bryan Kaehr1
Sandia National Laboratories1,University of Colorado Boulder2
Liquid crystal elastomers (LCE) are an active material that can provide rapid, reversible, and programmable actuation. In recent years, the additive manufacturing (AM) of LCE has gained attention as a facile means to both fabricate and program the actuation response. Nonetheless, LCE printing patterns and subsequent actuation directions have been limited to planar structures or simple geometries. To overcome this, we propose a method for printing of LCE in a laponite support gel which enables multi-planar, complex LCE structures. Furthermore, the LCE printing process can be monitored using in-situ measurements to build a machine learning model which will inform print parameters and final actuation properties. Lastly, we will provide demonstrations of how using machine learned printing parameters on multi-planar geometries will enable unprecedented multi-dimensional actuation profiles.