Owing to freedom of design, simplicity, and ability to handle complex structures, additive manufacturing (AM) or 3D printing of ceramics represents a promising enabling technology and has already been used to produce geometrically complex ceramic components and ceramic metamaterials. Consequently, novel applications for additively manufactured ceramics, which leverage their structural, high temperature, and chemical-resistant properties, have been proposed in areas ranging from electrical engineering and micro/nanoelectronics to chemical engineering to biology. Polymer derived ceramics (PDCs) represent a relatively new class of materials within additive manufacturing. PDCs enable the development of ceramic parts patterned via low-cost polymer 3D printing methods followed by pyrolysis in a high temperature process in which the polymer itself forms a ceramic often in the absence of any ceramic filler. PDCs have served as a feedstock for various 3D printing techniques for which a wide range of physiochemical factors can be tailored to optimize the ceramic manufacturing processes. In particular, the silicon and carbon-rich polymeric microstructure of PDCs offers a high degree of tunability and potential to achieve a closely defined combination of functional, thermomechanical, and chemical properties. In this review, we cover mechanisms underlying the design and manufacture of ceramics via 3D printing and pyrolysis of preceramic polymers, focusing on chemical formulations, printing technologies, and the mechanical performance of the ceramic network from microscale to scale. We also summarize experimental data from the literature and present qualitative and quantitative comparisons between different AM routes to provide a comprehensive review for 3D printing of PDCs and to highlight potential future research.

Brittleness is a major limitation of polymer-derived ceramics (PDCs). Different concentrations of three nanofillers (carbon nanotubes, Si₃N₄and Al₂O₃nanoparticles) were evaluated to improve both toughness and modulus of a commercial polysilazane (PSZ) PDC. The PSZs were thermally cross-linked and pyrolyzed under isostatic pressure in nitrogen. A combination of mechanical, chemical, density, and microscopy characterizations was used to determine the effects of these fillers. Si₃N₄and Al₂O₃nanoparticles (that were found to be active fillers) were more effective than nanotubes and improved the elastic modulus, hardness, and fracture toughness (J_IC) of the PDC by ~ 1.5 ×, ~ 3 ×, and ~ 2.5 ×, respectively. Nanotubes were also effective in maintaining the integrity of the samples during pyrolysis. The modulus and hardness of PDCs correlated positively with their apparent density; this can provide a fast way to assess future PDCs. The improvement in fracture toughness was attributed to crack deflection and bridging observed in the micro-indentation cracks in the modified PDCs. The specific toughness of the modified PDCs was 4 × higher than that of high-purity alumina, and its specific modulus reached that of commercially available technical ceramics. These PDCs can also easily take different shapes and therefore are of interest in protective armor, propulsion, thermal protection, device packaging and biomaterial systems.

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation of the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

Cracks are one of the primary forms of damage that can lead to the catastrophic failure of metallic structures. This study focuses on the application of epoxy nanocomposite thin film sensors for continuous monitoring of crack evolution in metallic structures. The core approach was to monitor the current (or resistance) change in these nanocomposite films, as cracks develop and propagate in the metallic host structure. Based on optical, electrical, and mechanical properties of epoxy resins modified with different contents of single-walled carbon nanotubes, two different nanocomposites (with 0.3 and 1.0 wt%) were chosen for the development of a crack sensor. The performance of the nanocomposite sensors was evaluated under tension–tension fatigue tests, on aluminum coupons with centrally located through thickness electrical discharge machining notches. Crack growth in the aluminum was found to transfer to the nanocomposite films in a stable mode. Once the crack was established, a linear correlation was found between the measured current and crack length with a slope of −10⁻¹¹ and −10⁻⁸ A/mm for 0.3 and 1.0 wt% nanocomposites, respectively. Contact between the asperities formed on the crack surfaces in the nanocomposite film while the crack was closed at small loads (<30% of maximum load) was found to be an important limiting factor causing a large variation in measured currents during each fatigue cycle. Hence, a normalized variable based upon current change during each cycle was defined, providing a more accurate measurement of the crack size, with a crack gauge factor of ∼0.04 mm⁻¹. In summary, the nanocomposite thin film sensor developed in this study offers both continuous crack growth monitoring and the possibility of strain sensing. The sensor is also suitable for visual inspection of the host structure due to the transparency of the developed nanocomposite film.

This study demonstrates the fabrication of high‐strength, lightweight polymer‐derived ceramics (PDCs) using silicon oxycarbide (SiOC)‐precursor formulations with liquid crystal display (LCD) vat photopolymerization (VPP) technology. Complex geometries, such as gyroids and stochastic lattices, are successfully 3D‐printed and evaluated under varying feature thicknesses and pyrolysis temperatures (800 °C and 1200 °C). Photorheology and thermogravimetric analysis (TGA) validated the efficient curing and pyrolysis characteristics of a printable precursor formulation based on vinyl methoxysiloxane homopolymer (VMM‐010), which demonstrated rapid curing, low viscosity, and compatibility with LCD 3D printing, ensuring precise layering and efficient resin removal. Micro‐CT scans confirmed its structural integrity and absence of voids, even in relatively thick components (≈3 mm). The VMM‐based PDC lattices achieved specific compressive strengths up to 9.4 MPa g⁻¹ cm³, a 50‐fold improvement over comparable lattices produced with a high‐porosity SiOC PDC, and exceptional high‐temperature stability, maintaining structural integrity after 2 h at 1500 °C. Compositional analysis revealed lower free carbon content and improved ceramic phase formation, driving the enhanced mechanical and thermal performance of the VMM‐based ceramic. These findings underscore the scalability, reliability, and superior performance of VMM formulations for LCD 3D printing, offering new possibilities for high‐performance ceramic applications in aerospace, automotive, and biomedical industries.