UC Berkeley

Additive manufacturing (AM) has had continued and growing appeal in academia and industry due to its ability to create customizable designs containing complex geometries and internal structures, reduce time to move from concept to final product for low volume production runs, and utilize a wide range of materials. One specific area of interest is the inclusion of additives into AM materials creating opportunities to produce parts with tailored material properties with specific functionality. For example, the addition of nanoparticles, such as carbon nanotubes and semiconductor nanocrystals, into polymers have lead to the creation of customizable micrometer sized strain gauges and light-emitting devices. While the additives provide many benefits, they lead to undesired changes to the material response during manufacturing that require modifying the process parameters to maintain print quality.

The focus of this work is the development of a computational modeling tool that captures the changes in the material response of nanoparticle-photopolymer composites for process parameter optimization in inkjet printing. This is achieved by introducing the photopolymerization process to the thermo-mechanical model within the particle finite element method (PFEM) framework. The simulation portion of this work is then broken into two parts. First, the effects of additives are investigated in two simple model problems, the deposition of a single droplet and stereolithography type process, designed to limit the influence of process parameters. Second, the effects of process parameters on a nanoparticle-photopolymer composite are studied to develop an objective function to characterize the results of the simulations.