Additive manufacturing (AM) has emerged as a transformative approach for advancing thermal management technologies, providing unprecedented freedom in design, material customization, and the implementation of novel thermal control strategies. This review presents a comprehensive overview of recent progress in AM-enabled thermal management, with an emphasis on enhancements in conductive, convective, boiling, and radiative heat transfer. AM facilitates the fabrication of complex architectures and composite materials with tailored thermal conductivities, substantially improving heat dissipation in diverse applications, including electronics, automotive systems, aerospace structures, and building technologies. Notably, recent developments in thermal metamaterials—such as structures capable of thermal cloaking and directional heat conduction—highlight the considerable potential of AM for manipulating complex thermal fields. Furthermore, the integration of phase change materials within AM-fabricated structures offers improved energy storage capacity and efficient thermal regulation. Future research should focus on the development of advanced composite materials, the integration of artificial intelligence for design optimization, the exploration of multifunctional metamaterials, and the advancement of sustainable and scalable AM processes. Hybrid and multimaterial AM techniques are particularly promising, enabling the fabrication of complex, functionally graded structures with precisely tailored thermal and mechanical properties. Addressing critical challenges—including structural integrity, microstructural control, material scalability, cost-effective production, and environmental sustainability—will further strengthen the role of AM in thermal management. In addition, the continued incorporation of high-fidelity computational simulations and real-time monitoring into AM workflows is expected to enhance process reliability and reproducibility. Expanding the range of AM applications to encompass lightweight and optically transparent polymer-based devices could unlock new avenues for thermal management in sensitive electronic and photonic systems.

The prevailing screening and qualification methodologies heavily depend on conventional manufacturing processes, which incur significant costs and prolonged lead times due to extensive physical testing. These challenges are also present in the growing field of additive manufacturing (AM), where numerous process parameters must be considered. However, the net-shape forming advantage of AM renders conventional screening and qualification methods inadequate. In the context of ongoing industrial digital transformation, a promising approach to enhancing process screening and qualification for metal AM is the adoption of a digital methodology tailored to the unique characteristics of this manufacturing technique. In this study, a convolutional neural network model is employed to extract features from images to predict material properties in laser-directed energy deposition (L-DED) processes. The model achieved a mean absolute percentage error of 2.3% and a root mean square error of 15.0 MPa for predicting ultimate tensile strength, with a prediction residual within ±1% for density. Unlike conventional approaches that rely on bulk or multilayer builds, this study uniquely demonstrates the feasibility of using early-stage single-track print features to predict final part properties with limited view and material involvement. This established model and workflow pave the way for highly efficient and low-cost property prediction in L-DED processes.

Thin-wall geometries produced by laser powder bed fusion combine high manufacturing efficiency, design flexibility, and cost-effectiveness for specialized applications. In such features, surface quality directly impacts dimensional accuracy and functional performance. This study investigates the effects of laser power, scan path, build orientation, and nominal gap distance on the top- and vertical-surface roughness, surface features, and dimensional error (DE) of 316L stainless steel thin walls. Optical microscopy was employed to characterize melt pool morphology and surface characteristics. Increasing laser power enlarges melt pools, promotes lateral migration, and induces dross formation on vertical surfaces, raising roughness and DE. Incorporating a contour scan with an inward offset reduces the scanned area, limits melt pool migration, and improves dimensional accuracy. Print orientation has a negligible influence on DE under the tested conditions, while small gaps may close entirely at high power due to large melt pools and migration. Compared to cubes fabricated with identical parameters, thin walls exhibit rougher top surfaces at high power, attributed to reduced track overlap, limited wetting from previous layers, and powder redistribution near vertical edges, whereas vertical-surface behavior remains similar. These findings provide practical guidelines for optimizing dimensional accuracy and surface quality in thin walls through coordinated control of process parameters and geometry.

There is growing interest in applying 3D printing technologies to environmental restoration, particularly for fabricating bio-inspired artificial reefs and printing coral skeletons to attract fish and support coral growth and survival. More recently, tissue engineering and 3D bioprinting strategies have been employed to develop biomimetic biomaterials that more closely replicate the natural coral microenvironment, including the incorporation of coral symbionts, to aid restoration efforts. In this study, we investigate the use of diverse ultrashort peptide- and biofunctionalized peptide-based bioinks to support bail-out polyp re-settlement and subsequent micropropagation. Among the 13 bioinks examined, eight demonstrated polyp biocompatibility and stability under seawater conditions. We focused on two Scleractinia species, Stylophora pistillata and Pocillopora verrucosa, and optimized a culture strategy for microencapsulated bail-out polyps following re-settlement, comparing a single-entity versus clustered-entity approach. These advancements lay the groundwork for polyp transplantation using biomimetic biomaterials. The top-performing bioinks were selected based on bioink underwater stability, polyp biocompatibility, and suitability for 3D bioprinting of polyps onto coral skeletons. This led to the development of a coral-inspired, polyp-containing bio-skin graft designed to promote coral tissue regeneration. Here, we report the first results demonstrating the use of bioinks for coral polyp microencapsulation and 3D bioprinting with ultrashort peptide-based bioinks to support coral regeneration and transplantation on coral skeletons.

The impact of powder flow characteristics on in situ nickel (Ni)-titanium (Ti) alloy formation within the laser powder bed fusion (LPBF) process is poorly understood. In this study, flow segregation patterns of Ni-Ti powder blends within the LPBF build chamber were examined and were found to be influenced by the substrate surface, build layout distribution, and particle size distribution. These segregation patterns significantly impacted relative density (RD) and elasto-caloric properties of in situ alloyed nitinol components, with regions of lower RD correlated with lower Ni content and higher phase transformation enthalpies than regions with higher Ni content. It was found that powder segregation rates between Ti and Ni particles were higher for rougher substrates, which also contained higher amounts of unmelted powder than smoother substrates. Furthermore, the position of the unmelted powder relative to the deposition arm sweep impacted powder segregation patterns throughout the build chamber. Powder segregation patterns in the LPBF deposition bed were also affected by differences in material density between Ni and Ti and interparticle cohesive forces. The insights gained from this work provide a route to achieving improved microstructural and chemical homogeneity of in situ alloyed nitinol, with tailored thermo-mechanical performance.