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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. 

In this paper, we demonstrate a method of measuring the flexural elastic modulus of ceramics at an intermediate (~millimeter) scale at high temperatures. We used a picosecond laser to precisely cut microbeams from the location of interest in a bulk ceramic. They had a cross-section of approximately 100 μm × 300 μm and a length of ~1 cm. They were then tested in a thermal mechanical analyzer at room temperature, 500 °C, 800 °C, and 1100 °C using the four-point flexural testing method. We compared the elastic moduli of high-purity Al2O3 and AlN measured by our method with the reported values in the literature and found that the difference was less than 5% for both materials. This paper provides a new and accurate method of characterizing the high-temperature elastic modulus of miniature samples extracted from representative/selected areas of bulk materials. 

Warm pressing of organosilicon polymers is often challenging due to the formation of cracks due to release of volatile compounds during the warm pressing process. The focus of the present study is to warm press crosslinked SMP‐10 powders into crack‐free compacts and to pyrolyze them to get bulk SiC monoliths. Crack formation during warm pressing is addressed by optimizing the crosslinking temperature, and the loss of formability of the powders crosslinked at higher temperatures is overcome with the use of uncured polymer as a binder. The crosslinking temperature of the preceramic polymer plays a crucial role in developing crack‐free green bodies. The amount of binder used is varied to study its effect on the bulk density of the pyrolyzed product. The warm pressed green bodies pyrolyzed at 1400 °C result in the formation of bulk silicon carbide ceramics and are characterized using X‐ray diffractometer and FTIR spectroscopy. Warm pressing is performed at a lower temperature than reported in the literature, and this limits the incorporation of oxygen during the warm pressing. 

Commonly used constitutive laws for crystalline and viscous materials have been compared to predict the densification behavior under hot‐pressing and sinter‐forging. Experimental results, from literature for one loading condition, have been used to extract the constitutive laws for amorphous and crystalline materials and, these in‐turn, have been used to predict behavior under a different set of loading conditions. Ideally, the constitutive parameters obtained from one set of loading conditions and thermal history should apply to a different set of conditions. However, there is a lack of systematic experimental studies in which this can be checked. In this paper, we use constitutive parameters obtained from one set of conditions to predict the densification response under a different set of loading conditions. For both sintering of amorphous and crystalline materials, we use two different constitutive parameters and compare the predictions of these for the case where experimental results are not available. In addition, the effect of temperature on densification behavior for stress‐assisted sintering has been investigated. It is shown that the two commonly used constitutive models for viscous sintering (Scherer and Skorohod–Olevsky) predict similar behavior for amorphous materials. However, for crystalline materials, the predictions of the Riedel–Svoboda and the Kuhn–Sofronis–McMeeking (KSM) models are different. Finally, it is shown that the dependence of the normalized densification on temperature, under constant heating rate conditions, with parameters obtained from isothermal experiments, is a good test for the models.