A hybrid multi-objective optimization of functional ink composition for aerosol jet 3D printing via mixture design and response surface methodology.

The limited electrical performance of microelectronic devices caused by low inter-particle connectivity and inferior printing quality is still the greatest hurdle to overcome for Aerosol jet printing (AJP) technology. Despite the incorporation of carbon nanotubes (CNTs) and specified solvents into functional inks can improve inter-particle connectivity and ink printability respectively, it is still challenging to consider multiple conflicting properties in mixture design simultaneously. This research proposes a novel hybrid multi-objective optimization method to determine the optimal functional ink composition to achieve low electrical resistivity and high printed line quality. In the proposed approach, silver ink, CNTs ink and ethanol are blended according to mixture design, and two response surface models (ReSMs) are developed based on the Analysis of Variance. Then a desirability function method is employed to identify a 2D optimal operating material window to balance the conflicting responses. Following that, the conflicting objectives are optimized in a more robust manner in the 3D mixture design space through the integration of a non-dominated sorting genetic algorithm III (NSGA-III) with the developed ReSMs and the corresponding statistical uncertainty. Experiments are conducted to validate the effectiveness of the proposed approach, which extends the methodology of designing materials with multi-component and multi-property in AJP technology.