Aerosol jet printing (AJP) technology recently gained considerable attention in an electronic manufacturing industry due to its ability to fabricate parts with fine resolution and high flexibility. However, morphology control has been identified as the main limitation of AJP process, which drastically affects the electrical performance of printed components. Even though previous researches have made significant efforts in process modeling to improve the controllability of the the printed line morphology, the modeling process is still inefficient under modified operating conditions due to the repeated experiments. In this paper, a knowledge transfer framework is proposed for efficient modeling of the AJP process under varied operating conditions. The proposed framework consists of three critical steps for rapid process modeling of AJP. First, a sufficient source domain dataset at a certain operating condition is collected to develop a source model based on Gaussian process regression. Then, the representative experimental points are selected from the source domain to construct a target dataset under different operating conditions. Finally, classical knowledge transfer approaches are adopted to extract the built-in knowledge from the source model; thus, a new process model can be developed efficiently by the transferred knowledge and the representative dataset from the target domain. The validity of the proposed framework for the rapid process modeling of AJP is investigated by case study, and the limitations of the classical knowledge transfer approaches adopted in AJP are also analyzed systematically. The proposed framework is developed based on the principles of knowledge discovery, which is different from traditional process modeling approaches in AJP. Therefore, the modeling process is more systematic and cost-efficient, which will be helpful to improve the controllability of the line morphology. Additionally, due to its data-driven based characteristics, the proposed framework can be applied to other additive manufacturing technologies for process modeling researches.

气溶胶喷射印刷（AJP）技术最近在电子制造行业中引起了广泛关注，因为它能够制造具有高分辨率和高灵活性的零件。但是，形态控制已被认为是AJP工艺的主要局限性，它极大地影响了印刷部件的电气性能。尽管先前的研究已经在过程建模中做出了很大的努力以改善印刷线路形态的可控性，但是由于重复的实验，在修改的操作条件下，建模过程仍然效率低下。在本文中，提出了一种知识转移框架，用于在各种操作条件下对AJP过程进行有效建模。拟议的框架包括AJP快速过程建模的三个关键步骤。第一的，收集了在特定操作条件下足够的源域数据集，以基于高斯过程回归来开发源模型。然后，从源域中选择具有代表性的实验点，以构建不同操作条件下的目标数据集。最后，采用经典知识转移方法从源模型中提取内置知识。因此，通过转移知识和来自目标领域的代表性数据集，可以有效地开发新的过程模型。通过案例研究研究了所提出的AJP快速过程建模框架的有效性，并且系统地分析了AJP中采用的经典知识转移方法的局限性。拟议的框架是根据知识发现的原理开发的，这与AJP中的传统过程建模方法不同。因此，建模过程更加系统化和具有成本效益，这将有助于提高线形的可控性。另外，由于其基于数据驱动的特性，因此所提出的框架可以应用于其他增材制造技术以进行过程建模研究。