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Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G’) and loss (G”) moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G’ and G”. We employed a multilayer perceptron (MLP) and successfully predicted G’ and G” from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student’s t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input–output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa. 

Nanocarbon 2024 aluminum composites with 0.5 vol. % and 1 vol. % of graphene nanoplatelets and 1 vol. % and 2 vol. % of activated nanocarbon were manufactured through induction casting. The effect of the reinforcements and heat treatment on the performance of the composites was examined. Analysis of the microstructure of the composites before heat treatment suggested the homogeneous dispersion of reinforcements and the absence of secondary carbide or oxide phases. The presence of carbon nanoparticles had a significant impact on the microstructural characteristics of the matrix. This behavior was further enhanced after the heat treatment. The mechanical and damping properties were evaluated with the uniaxial compression test, micro Vickers hardness test, and dynamic mechanical analysis. The yield strength and ultimate strength were improved up to 28% (1 vol. % of graphene nanoplatelets) and 45% (0.5 vol. % of graphene nanoplatelets), respectively, compared to the as-cast 2024 aluminum. Similarly, compared to the heat-treated 2024 aluminum, the composites increased up to 56% (0.5 vol. % of graphene nanoplatelets) and 57% (0.5 vol. % of graphene nanoplatelets) in yield strength and ultimate strength, respectively. Likewise, the hardness of the samples was up to 33% (1 vol. % of graphene nanoplatelets) higher than that of the as-cast 2024 aluminum, and up to 31% (2 vol. % of activated nanocarbon) with respect to the heat-treated 2024 aluminum. The damping properties of the nanocarbon–aluminum composites were determined at variable temperatures and strain amplitudes. The results indicate that damping properties improved for the composites without heat treatment. As a result, it is demonstrated that using small volume fractions of nanocarbon allotropes enhanced the mechanical properties for both with- and without-heat treatment with a limited loss of plastic deformation before failure for the 2024 aluminum matrix. 

This paper reports the study of hollow microballoon-filled epoxy composites also known as syntactic foams with various volume fractions of microballoons. Different mechanical and thermomechanical investigations were carried out to study the elastic and viscoelastic behavior of these foams. The density, void content, and microstructure of these materials were also studied for better characterization. In addition to the experimental testing, a representative 3D model of these syntactic foams was developed to further investigate their elastic behavior. The results indicate that changes in the volume percentage of the microballoons had a substantial impact on the elastic and viscoelastic behavior of these foams. These results will help in designing and optimizing custom-tailored syntactic foams for different engineering applications. 

6061 aluminum composites with 0.5 and 1 vol. % graphene nanoplatelets as well as 1 and 2 vol. % activated nanocarbon were manufactured by a powder metallurgy method. Scanning electron microscopy and Raman spectroscopy were used to study the morphology, structure, and distribution of nanocarbon reinforcements in the composite samples. Density Functional Theory (DFT) calculations were performed to understand the aluminum-carbon bonding and the effects of hybridized networks of carbon atoms on nanocarbon aluminum matrix composites. Scanning electron microscopy showed the good distribution and low agglomeration tendencies of nanoparticles in the composites. The formation of secondary phases at the materials interface was not detected in the hot-pressed composites. Raman spectroscopy showed structural changes in the reinforced composites after the manufacturing process. The results from Density Functional Theory calculations suggest that it is thermodynamically possible to form carbon rings in the aluminum matrix, which may be responsible for the improved mechanical strength. Our results also suggest that these carbon networks are graphene-like, which also agrees with the Raman spectroscopy data. Micro-Vickers hardness and compressive tests were used to determine the mechanical properties of the samples. Composites presented enhanced hardness, yield and ultimate strength compared to the 6061 aluminum alloy with no nanocarbon reinforcement. Ductility was also affected, as shown by the reduction in elongation and by the number of dimples in the fractured surfaces of the materials. 

Embedding carbon in metals has long been known to enhance the mechanical properties of metal carbon composites. We report the possibility of growing Al–C composites by the hot isostatic pressing method, with carbon embedded into an Al lattice in graphitic form without the formation of Al4C3. Raman spectroscopy confirms the formation of sp2-hybridized carbon clusters in the aluminum lattice. The bulk moduli of the samples were measured to be between 60 and 100 GPa. From the results of first principles density functional theory calculations, we show that the formation of sp2-hybridized carbon clusters is more stable than having isolated C scatterers in aluminum. Our results show that the extended network of C clusters shows a higher bulk modulus while isolated scattering centers could lower the bulk modulus. We explain this behavior with the analysis of total charge distribution. Localization of charge density decreases materials’ ability to respond to external stress, thus showing a reduced bulk modulus. Some defect configuration may reduce the symmetry while others keep the symmetry of the host configuration even for the same chemical composition of Al–C composites. 

Additive manufacturing such as vat photopolymerization allows to fabricate intricate geometric structures than conventional manufacturing techniques. However, the manufacturing of lightweight sandwich structures with integrated core and facesheet is rarely fabricated using this process. In this study, photoactivatable liquid resin was used to fabricate sandwich structures with various intricate core topologies including the honeycomb, re-entrant honeycomb, diamond, and square by a vat photopolymerization technique. Uniaxial compression tests were performed to investigate the compressive modulus and strength of these lightweight structures. Sandwich cores with the diamond structure exhibited superior compressive and weight-saving properties whereas the re-entrant structures showed high energy absorption capacity. The fractured regions of the cellular cores were visualized by scanning electron microscopy. Elastoplastic finite element analyses showed the stress distribution of the sandwich structures under compressive loading, which are found to be in good agreement with the experimental results. Dynamic mechanical analysis was performed to compare the behavior of these structures under varying temperatures. All the sandwich structures exhibited more stable thermomechanical properties than the solid materials at elevated temperatures. The findings of this study offer insights into the superior structural and thermal properties of sandwich structures printed by a vat photopolymerization technique, which can benefit a wide range of engineering applications.