In this paper, predictive models are developed for inkjet printed features regarding their electrical and mechanical performance and to help reduce the initial process time in selecting print parameters. Printed electronics are continuously getting immense interest with a steady increase in its areas of end applications. The process generally involves controlled deposition of material on a substrate to additively build the required structure. Due to the nature of additive printing, benefits such as reduced time to manufacturing and the possibility of flexible and conformable electronics can be easily achievable. Under the umbrella term of additive printing, Inkjet printing is one technique that is sometimes known as the workforce of mass manufacturing due to the number of nozzles ranging in hundreds or even thousands, with which it can print the structures. The process, also known as drop-on-demand, involves the deposition of liquid ink droplets as per the required structure. However, for deposition, inkjet requires control of certain process parameters that impact the print resolution and, thus, the printed material properties. Thus, it is important to have a predictive framework that helps select those significant parameters. Silver Nanoparticle-based ink is utilized that is compatible with the viscosity range allowed in the printer. For the predictive framework development, a statistical approach is implemented that consists of a design-of-experiments (DOE) matrix with significant parameters that have a major impact on the resolution and properties of the material. The study’s response variables consist of the printed feature’s electrical and mechanical properties. The aim of this study is to provide statistical models that can be used with Inkjet process parameters as an input to predict the properties of the final printed feature.

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