Additive manufacturing is rapidly evolving and opening new possibilities for many industries. This article gives an overview of the current status of additive manufacturing with polymers and polymer composites. Various types of reinforcements in polymers and architectured cellular material printing including the auxetic metamaterials and the triply periodic minimal surface structures are discussed. Finally, applications, current challenges, and future directions are highlighted here.

Triply periodic minimal surfaces (TPMS) are mathematically defined minimal surfaces that exhibit zero mean curvature and repeat periodically along all three Cartesian axes. They integrate mathematically defined geometry with extensive functional adjustability. Their smooth, non-self-intersecting topology enables systematic control over relative density and improves load transfer efficiency within the lattice. Their large surface area-to-volume ratios further enhance specific energy absorption (SEA) and enable diverse functional uses. Recent developments in additive manufacturing (AM) have made it easier to create TPMS structures. As a result, they are now considered as the architected materials that combine biological, thermal, and mechanical functions within a single framework. This study presents a comprehensive overview of the major TPMS structures. It further highlights several AM techniques used for their fabrication and provides a critical evaluation of how geometric design, relative density, and post-processing influence their mechanical and thermal performances. This work also discusses recent developments in graded and hybrid TPMS structures. It further identifies the main challenges and future research directions related to multi-material additive manufacturing and data-driven topology optimization.

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.

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.

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.