Meniscus motion and drop formation in inkjet printing

Inkjet technology is a very precise and highly flexible technique, which produces microscale drops which are jetted onto a carrier substrate. This technology is well known for e.g. document printing, but is still pushed to generate smaller droplets with a higher drop formation rate. Therefore a firm understanding of the drop formation process and the causes of inkjet defects are of great importance. Drop-on-Demand inkjet printers are actuated using pressure waves, which are create by for example a piezo acoustic actuator. The meniscus is deformed by the pressure wave to produce a microdroplet. In this thesis we aim at creating methods and improving existing methods, to understand the formation of the microdroplets. We first focus on the motion of the meniscus in the nozzle and accordingly on the droplet formation process. We review experimental methods to obtain flow information from within the nozzle, e.g. volume and velocity of the fluid and the shape of the meniscus. These are challenging measurements due to the small scale of the nozzle and the high velocity of the fluid. Asymmetric meniscus shapes prior to the formation of the drops, can have non-desired asymmetric effects on the formation of drops. We investigate the acceleration thresholds for the acceleration of the meniscus, above which asymmetric instabilities of the meniscus are expected. For studying the microdrop formation process we make use of ultra high speed imaging, i.e. an illumination time shorter than 10ns, to image the small drops without motion blur. We introduce a method to extract the velocity profile inside a single microdroplet during its formation. A novel experimental approach is used to capture two detailed images of the very same droplet with a small time delay (600ns). By accurately determining the volume distribution of the droplet the velocity within the droplet are resolved. Computational Fluid Dynamics (CFD) simulations are a meaningful contribution to inkjet research, since the drop formation can be predicted for a range of acoustic and fluidic parameters. We use the measured velocity profiles in the microdrops to perform a detailed comparison of the predictions of the CFD droplet formation models.