The synthesis and manufacturing of polymer nanocomposites have garnered interest in recent research and development because of their superiority compared to traditionally employed industrial materials.

The synergistic integration of nanocomposites and 3D printing has opened a gateway to the future and is soon expected to surpass its rivalry with traditional manufacturing techniques. However, there is always a challenge associated with preparing a nanocomposite resin for polymerization-based 3D printing, which is the agglomeration of nanoparticles. Due to the high surface-area-to-volume ratio, the nanoparticles form clusters in the composite matrix, which affects the final properties. This paper aims to analyze the effects of graphene oxide (GO) dispersion on the mechanical and thermal properties of 3D-printed nanocomposites. In particular, a well-dispersed sonication dispersion route is employed for analyzing high and poor GO dispersions and their effects on different properties. After different microscopic analyses and testing, the optimum sonication condition was 30 min at an amplitude of 70%. In terms of mechanical properties, both tensile and compression strength first increased and then decreased gradually with different dispersions as well as varying GO concentrations. Furthermore, there was less or no effect on thermal stability. GO of 0.05 wt.% had the highest compression and tensile strength, while beyond 0.05 to 0.5 wt.%, both strengths reduced slowly. These 3D-printed nanocomposites have found their application in automotive, sports, and biomedical fields.

Additive manufacturing (AM) of ceramics presents both exciting opportunities and significant challenges, particularly with the laser‐based AM processes. Ceramics are known for their special properties, such as high strength, corrosion resistance, and temperature stability, but their inherent brittleness and high processing demands make AM more complex. This review provides an updated overview of the most common AM techniques for ceramics, including direct energy deposition, binder jetting, laminated object manufacturing, and material extrusion‐based techniques. However, the focus is placed on the laser powder bed fusion (LPBF) of ceramics, a technique that has gained increasing attention for its ability to fabricate complex ceramic parts with enhanced quality. The review delves into the key causes of critical defects commonly observed in LPBF, such as porosity, cracking, spattering, and surface roughness. Recent advancements in addressing these issues are discussed, along with the limitations of current defect prevention strategies. Furthermore, the review provides an updated analysis of the mechanical properties of LPBF‐fabricated ceramics, giving insights into how processing parameters influence the performance of ceramic LPBF‐printed parts. Modeling and simulation techniques are also reviewed, highlighting their role in enhancing understanding of ceramic behavior during LPBF. Overall, this review highlights recent progress and current challenges in ceramic AM techniques, while exploring future research opportunities, such as process optimization and defect prevention strategies.

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.

Laser powder bed fusion is a laser-based additive manufacturing technique that uses a high-energy laser beam to interact directly with powder feedstock. LPBF of oxide ceramics is highly desirable for aerospace, biomedical and high-tech industries. However, the LPBF of ceramics remains a challenging area to address. In this work, a new slurry-based approach for LPBF of ceramic was studied, which has some significant advantages compared to indirect selective laser sintering of ceramic powders. LPBF of Al₂O₃ was fabricated at different MgO loads up to 80 wt%. Several specimens on different laser powers (70 W–120 W) were printed. The addition of magnesia influenced the microstructure of the alumina ceramic significantly. The findings show that when the laser power is high and the magnesia load is low, the surface quality of the printing parts improves. It is feasible to produce slurry ceramic parts without binders through LPBF. Furthermore, the effects of SiC and MgO loads on the microstructure and surface morphology of alumina are compared and analysed.