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Abstract 3D printing (3DP) technologies have transformed the processing of advanced ceramics for small‐scale and custom designs during the past three decades. Simple and complex parts are designed and manufactured using 3DP technologies for structural, piezoelectric, and biomedical applications. Manufacturing simple or complex geometries or one‐of‐a‐kind components without part‐specific tooling saves significant time and creates new applications for advanced ceramic materials. Although development and innovations in 3DP of ceramics are far behind compared with metals or polymers, with the availability of different commercial machines in recent years for 3DP of ceramics, exponential growth is expected in this field in the coming decade. This article details various 3DP technologies for advanced ceramic materials, their advantages and challenges for manufacturing parts for various applications, and perspectives on future directions. We envision this work will be helpful to advanced ceramic researchers in industry and academia who are using different 3DP processes in the coming days. 

Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy.