A hybrid multi-objective optimization of aerosol jet printing process via response surface methodology

Abstract

Aerosol jet printing (AJP) is an emerging 3-dimensional (3D) printing technology to fabricate customized and conformal microelectronic components on various flexible substrates. Although the AJP technology has the capability of depositing fine features, the inherent contradiction between the printed line thickness and line edge roughness has a great impact on the printed line quality. In this paper, a novel hybrid multi-objective optimization method is proposed to optimize the overall printing quality of AJP based on a determined operating window. The proposed approach consists of a central composite design (CCD), a response surface methodology, a desirability function approach and a non-dominated sorting genetic algorithm III (NSGA-III). In the proposed approach, the response surface methodology is combined with the CCD to investigate and quantify the correlations between the printed line features and the key process parameters. And the conflicting relationship between the printed line edge roughness and line thickness is identified by the CCD derived response surface models (RSMs). Then the 2D optimal operating windows are determined to minimize the inherent contradiction between the printed line features at a certain print speed by the desirability function approach. Following that, the derived RSMs and the corresponding statistical uncertainty are jointly driven with a NSGA-III to systematically optimize the overall printing quality in 3D design space by more robust manners. The experimental results demonstrate that the proposed hybrid multi-objective optimization approach is beneficial to minimize the conflict between the printed line features, hence the lines can be produced with low line edge roughness and sufficient line thickness. Different from a traditional trial-and-error method in AJP, the proposed printing quality optimization approach is developed based on the principles of statistical modeling, analysis of variance and global optimization. Therefore, the proposed printing quality optimization approach is more efficient and systematic. Moreover, the data-driven based characteristic makes the proposed approach applicable to other multi-objective optimization researches in additive manufacturing technologies.