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Abstract Electrohydrodynamic (EHD) printing is a versatile process that can be used to pattern high-resolution droplets and fibers through the deposition of an electrified jet. This highly complex process utilizes a coupled hydrodynamic and electrostatic mechanism to drive the fluid flow. While it has many biomedical, electronic, and filtration applications, its widescale usage is hampered by a lack of detailed understanding of the jetting physics that enables this process. In this paper, a numerical model is developed and validated to explore the design space of the EHD jetting process, from Taylor cone formation to jet impingement onto the substrate, and analyze the key geometrical and process parameters that yield high-resolution structures. This numerical model applies to various process parameters, material properties, and environmental factors and can accurately capture jet evolution, radius, and flight time. It can be used to better inform design decisions when using EHD processes with distinct resolution requirements. 

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.