ABSTRACT Laser Powder Bed Fusion (PBF-LB/M) is an emerging technology that enables excellent dimensional accuracy and mechanical performance. In this work, we present a systematic approach to investigate roughness engineering for metal–polymer bonding. By carefully tuning the process, the averaged surface roughness (Ra) of the printed part was precisely modulated while maintaining the relative density (> 99%). The resultant surfaces, ranging from a polished surface (Ra = 2.1 µm) to as-built morphologies with small (26.7 µm), large (56.9 µm), and severe roughness (65.9 µm), were investigated for metal–polymer bonding. Lap-shear tests with representative silicone polymers (EcoFlex, Dragon Skin, and PDMS) revealed that this engineered roughness achieved superior adhesion strength between metal and polymer. Specifically, the large roughness engineered condition (Ra = 56.9 µm) yielded adhesive strengths of 220.8, 321.0, and 717.2 kPa for EcoFlex, Dragon Skin, and PDMS, respectively. Compared to non-treated polished surfaces, this strategy led to a substantial increase in adhesive strength by 214.3% (EcoFlex), 228.5% (Dragon Skin), and 228.7% (PDMS). Finally, the multi-roughness regions were fabricated within a single part, potentially offering the site-specific functional service requirements. This approach establishes a straightforward manufacturing-integrated strategy to achieve high-performance metal–polymer joints, enabling design-flexible multi-material architectures for functional hybrid systems.

ABSTRACT Multi-material additive manufacturing enables the integration of dissimilar materials within a single component, yet achieving a compositionally gradient interface within individual layers remains challenging. This study presents a novel approach using a self-developed ultrasonic nozzle system for programmable, high-precision powder deposition. Using an interdigitated interface design with a layer-wise offset arrangement between 316L stainless steel and IN718 superalloy, we demonstrate that the alternating powder layout, together with intense melt-pool stirring during laser powder bed fusion, promotes thorough material intermixing and creates ∼1 mm wide compositionally gradient interface. The interface exhibits continuous transitions in grain growth, elemental distribution, and microhardness, confirming the formation of a continuous gradient between materials. Notably, the width of the gradient interface is tailorable through deposition control. This work demonstrates the feasibility of fabricating compositionally gradient interfaces via designed layer-wise powder deposition, providing a controllable approach for manufacturing functionally gradient components.

ABSTRACT The dual roles of Mg in the room-temperature strengthening and high-temperature softening of LPBF-Processed AlMnScZr alloys are systematically elucidated. The addition of Mg promotes compositional supercooling during solidification, transforming the as-built columnar grains of the Mg-free alloy into a refined bimodal structure comprising alternating coarse and fine equiaxed regions. Mg also accelerates the formation of primary Al6Mn precipitates, increasing their size from ∼43.7 nm in the Mg-free alloy to 54.0 and 114.6 nm in alloys containing 1.5 and 3.5 wt.% Mg, respectively. During aging, Mg addition significantly enhances the dispersion and number density of secondary Al6Mn particles, while exerting negligible influence on the formation of nanoscale Al3(Sc, Zr) precipitates (∼3 nm). The synergistic effects of solid-solution strengthening, precipitation hardening, and grain refinement yield superior room-temperature properties, achieving ultimate tensile strengths of 538 MPa (1.5 wt.% Mg) and 626 MPa (3.5 wt.% Mg). However, Mg addition also accelerates thermal softening in the AlMnScZr alloy. For instance, the yield strength at 300 °C drops to 108 MPa in the 3.5 wt.% Mg alloy, compared to 198 MPa in the base alloy. This strength loss is identified as being due to Mg-promoted coarsening of Al6Mn particles during thermal exposure.

ABSTRACT This study systematically investigates the microstructure of wire-arc additively manufactured heat-resistant aluminium alloys through crystallographic parameter analysis, focusing on gradient evolution mechanisms and their implications. The distinct Ni incorporation methods yield different microstructure: The ACN specimens characteristic gradients transitioning from coarse-grained (CG) to fine-grained (FG) regions, evolving from high-dislocation-density columnar grains (exhibiting elevated strain and predominant low-angle boundaries) to low-dislocation-density equiaxed grains (dominated by high-angle boundaries and enhanced Schmid factors). Conversely, ACNP specimens achieve homogeneous equiaxed microstructures through rapid dissolution of interlayer Ni powder that forms microscale liquid pools. Complementary to the crystallographic analysis, the ACNP specimens exhibiting uniformly dispersed micro-pores, Weakened the stress concentration effect, further homogenising plastic flow at both micro- and meso-scales. The crystallographic gradient analysis establishes direct correlations between thermal history, dislocation substructures, and boundary evolution, revealing how powder-induced nucleation fundamentally transforms solidification dynamics by replacing directional growth with isotropic grain development – a novel insights for next-generation WAAM-processed heat-resistant alloys requiring balanced thermomechanical performance.

ABSTRACT NiTi shape memory alloys have been widely recognised for their functional properties, especially the elastocaloric effect. However, the elastocaloric performance is often limited by intrinsic phase transformation hysteresis. Herein, we propose a novel manufacturing strategy, integrating in situ and post-processing to achieve microstructure-engineered reinforcement. By precisely controlling Ni content and thermal history, we induce inhomogeneous Ni4Ti3 precipitation phase within the NiTi matrix, forming tailored phase distribution. After aging, the R-phase nanodomain structure was developed, which minimises energy dissipation by suppressing transformation hysteresis. The Ni4Ti3 phase and heterogeneous microstructure synergistically enhance the matrix’s phase transformation enthalpy and boost the adiabatic temperature change (ΔT ad). This manufacturing strategy yields a 231.6% improvement in the material coefficient of performance (COP mat)for the L-PBFed NiTi alloy. This work demonstrates how precipitate-matrix interactions and multiscale microstructure engineering can optimise functional material, offering a new paradigm for high-efficiency elastocaloric cooling components.

ABSTRACT Laser-based metal additive manufacturing (MAM), particularly laser powder bed fusion (LPBF) and laser directed energy deposition (L-DED), is a transformative technology enabling the production of complex, high-performance metal components with precision and minimal waste. However, its wider adoption is hindered by challenges in ensuring repeatability and consistency due to the complex, dynamic nature of the process. Closed-loop systems with in-situ monitoring and control have gained attention for quality assurance, where machine learning (ML) models are crucial for correlating sensor data with process state diagnosis and facilitating control actions. This review adopts an ML perspective to elucidate the role of ML in closed-loop control systems. It first outlines sensor data types and preprocessing methods used as ML datasets, then discusses roles and applications of ML models for in-situ monitoring, highlighting limitations and potential solutions. An ML-based closed-loop control framework is also presented, with emphasis on the integration of ML in the control component. Finally, future development directions of advanced ML approaches are envisioned. This review provides new insights into enhancing the role of ML models in laser-based MAM closed-loop control systems, serving as a crucial aid in the development of intelligent control systems.

ABSTRACT Real-time prediction of vertical surface roughness in laser powder bed fusion (LPBF) is essential for process control and quality assurance, yet it remains largely unexplored due to view-blocking by loose powder in the machine bed. This study introduces the first integrated framework that combines in-situ photodiode monitoring with machine learning (ML) to predict sidewall roughness during fabrication. A high-speed photodiode sensor captures melt pool intensity signals near vertical surfaces, which are processed into time- and frequency-domain features. These features, together with process parameters, serve as inputs to ML models, while post-process surface roughness measurements (Sa), obtained via laser scanning confocal microscopy, are used as outputs during training. Once trained, the model can then be applied in real-time to predict roughness directly from photodiode signals acquired during printing, enabling side-specific monitoring without additional measurement steps. Among the five models evaluated, Random Forest (RF) and eXtreme Gradient Boosting (XGB) achieved the highest predictive accuracy, with RF improving from R 2 = 0.35 (parameters only) to R 2 = 0.78 when in-situ features were included. This framework demonstrates that photodiode-based monitoring, coupled with ML, enables reliable, side-specific, real-time prediction of vertical surface roughness in LPBF, offering a pathway towards adaptive quality control and reduced post-processing.

ABSTRACT Thermosetting resins are highly valued in industrial applications for their lightweight, high-strength, and heat-resistant properties. Nevertheless, conventional moulding techniques and current additive manufacturing processes either encounter limitations in geometric complexity, necessitate post-curing treatments, or confront challenges in material processability. This study introduces a novel rapid in-situ thermal curing (RITC) 3D printing process designed for the efficient fabrication of engineering-grade bisphenol-A epoxy resin components. By systematically conducting parametric studies to optimise material formulations and key printing parameters, including heater temperature, moving speed, hatch distance and layer thickness, the RITC process achieves synchronised rapid curing during the printing process, thereby enabling the fabrication of multi-layer 3D objects. The 3D-printed samples achieved tensile strength and modulus of 35.7 ± 5.4 MPa and 0.93 ± 0.16 GPa, respectively, alongside flexural strength and modulus of 69.5 ± 6.9 MPa and 2.85 ± 0.29 GPa, which were comparable to those of the mould-casted counterparts. Differential scanning calorimetry (DSC) analysis confirmed a high degree of curing of 99.8%, while scanning electron microscopy (SEM) examination revealed superior interfacial bonding with minimal porosity. This innovative RITC method not only eliminates the need for post-curing but also demonstrates compatibility with thermosetting resins characterised by extended pot life at ambient temperatures and accelerated curing kinetics under elevated thermal conditions.

ABSTRACT This study explores the additive manufacturing (AM) of silicon carbide (SiC) ceramics featuring gradient triply periodic minimal surface (TPMS) architectures fabricated using vat photopolymerization, a high-resolution AM technique. We systematically evaluate the thermal performance of three TPMS topologies – Schoen gyroid (38 mm pores), Neovius surface (20 mm), and Schwarz diamond (14 mm) – under varying hotspot dimensions (1.5–2.5 cm). Results demonstrate that gradient TPMS configurations significantly improve thermal resistance by modulating porosity distribution while maintaining structural volume. Specifically, the Schoen gyroid achieves optimal thermal insulation at 1.5 cm hotspots, stabilising surface temperatures at 250°C, whereas the Schwarz diamond minimises conductive losses at 2 cm hotspots, reducing temperatures to 280°C. Sintering-induced defects, including gas-entrapped bubbles and stress-driven cracks, are identified as critical disruptors of heat transfer pathways in larger hotspots (2.5 cm), causing localised temperature deviations. To address this, we propose a hierarchical checkerboard design integrating in-plane Schoen gyroid layers (for radiative-dominated 1.5/2.5 cm hotspots) and cross-plane Schwarz diamond layers (for conduction-critical 2 cm hotspots). This hybrid configuration adaptively confines heat flux through synergistic insulation mechanisms. Collectively, this work establishes a defect-mitigated design paradigm for TPMS-based SiC ceramics, advancing their utility in aerospace thermal protection systems subjected to heterogeneous thermal loads.

ABSTRACT The materials and design of scaffolds play a crucial role in promoting bone regeneration in alveolar bone defects. Extracted natural teeth have been considered potential graft biomaterials for treating bone defects. This study aimed to verify the feasibility and effectiveness of repairing alveolar bone defects using extracted natural teeth through biomechanical modelling additive manufacturing preparation and in vitro mineralisation experiments. Extracted natural teeth were collected to prepare standardised bioceramic slurry and evaluate the effect of digital light processing (DLP) process parameters including dispersion dosage solids loading and sintering temperatures. The results demonstrated that 50 vol% solid loading slurry with 2 wt.% dispersant dosage was regarded as the optimum slurry and 1200°C was the optimum sintering temperature. Biomechanical properties of four scaffold structures (trabecular diamond gyroid hexagonal void) were compared by Finite Element Analysis (FEA) Computational Fluid Dynamics (CFD) and compression tests. The results determined that G-scaffold exhibited excellent osteogenic space maintenance and mass transport properties. In vitro mineralisation experiments were conducted to validate the apatite-forming capacity of extracted natural teeth scaffolds. In conclusion extracted natural teeth are available to fabricate bioceramic scaffolds via DLP-based additive manufacturing techniques and exhibit superior clinical potential for the repair of alveolar bone defects.