This study investigates the harmonic response of acoustically functional geometries fabricated via additive manufacturing, with the aim of replicating the vibrational characteristics of natural wood. Utilizing Finite Element Modelling (FEM), various complex structures were designed and analyzed to determine their dynamic behavior under harmonic excitation. The materials under investigation include Nylon12, processed through Selective Laser Sintering (SLS) as a potential substitute for traditional tonewoods in acoustic applications. Emphasis was placed on preserving critical resonance frequencies and frequency response ranges typically associated with wood-based components. The results demonstrate that tailored internal geometries can significantly influence modal behavior, offering a viable pathway toward sustainable and tunable acoustic materials. This research bridges material science and acoustic engineering by proposing an efficient modeling framework for evaluating and optimizing additively manufactured alternatives to wood.

Augmented reality (AR) is rapidly emerging as a crucial real-time interface for data visualisation and decision-making in additive manufacturing (AM). This systematic review screened 71 Web of Science records, analysing 15 empirical studies (2021–2025) on AR-enhanced 3D-printing workflows. Reported prototypes, most frequently based on Microsoft HoloLens and MQTT-enabled data streams, demonstrate significant operational improvements such as shorter design cycles, reduced printer downtime and decreased energy consumption per part. Additionally, early fault detection via YOLOv7 overlays and holographic analytics improved print quality and training retention, though studies predominantly used small laboratory samples. Persistent challenges include limited fields of view, latency up to 8 seconds, and proprietary firmware hindering bi-directional data exchange. Interpreting these findings using an extended Technology Acceptance Model indicates that open printer architectures and AI-driven context-sensitive guidance systems are essential for advancing AR-AM from isolated demonstrations to scalable industrial practice.

Microbial colonization on 3D-printed zirconia restorations may aggravate plaque accumulation and periodontal inflammation. While additive manufacturing (AM) parameters significantly influence surface roughness and morphology, evidence regarding their impact on bacterial adhesion remains unclear. This study investigated the effects of AM technologies and build angles on the surface characteristics and initial microbial adhesion of 3D-printed zirconia. Zirconia discs were fabricated using material jetting (MJ, 10μm layer thickness) and digital light processing (DLP, 30μm layer thickness) technologies with three build angles (0°, 45°, and 90°), respectively (n = 25 per group). The surface topographic features and roughness were analyzed using scanning electron microscopy and laser scanning microscopy, respectively. The surface wettability was evaluated via water contact angle measurements. Streptococcus gordonii (S. gordonii) was used to assess bacterial adhesion, which was evaluated via colony-forming unit counts and visualized through SEM imaging. Statistical analysis involved two-way ANOVA and post hoc Tukey tests, with significance threshold set at p < 0.05. AM technologies and build angle significantly affected surface characteristics, with significant interactions observed for roughness (p < 0.05). DLP-45° showed the roughest surface, while DLP-0° was the smoothest. Water contact angle varied significantly with both factors (p < 0.05), with MJ-45° showing the highest wettability. For S. gordonii adhesion, a significant interaction between AM methods and build angle was found (p < 0.05), and AM methods showed a main effect (p = 0.0104), while build angle alone was not significant (p = 0.0642). The least adhesion occurred in MJ-45° and DLP-0°, with no consistent correlation between roughness and bacterial adhesion. AM technologies and build angle affected S. gordonii adhesion to zirconia surfaces. DLP printing at 0° and MJ printing at 45° were associated with significantly reduced bacterial counts, presenting a clinically approach to minimize initial plaque formation and support the long-term periodontal success of 3D-printed zirconia restorations.

Additive manufacturing (AM) offers significant potential but faces challenges in controlling rapid solidification processes due to thermal conditions. The application of magnetic fields provides a promising path to influence liquid metal behavior during solidification. Thermoelectromagnetic convection (TEMC) is one of the mechanisms by which an applied static magnetic field can induce melt flow, where thermal gradients at the solid–liquid interface generate thermoelectric currents, and in the presence of an external magnetic field induce Lorentz force that drives liquid convection, leading to enhanced heat transfer. This study investigates the impact of moderate static magnetic fields on the laser melting process of a Sn-10%wt.Pb alloy. It is found that applying a magnetic field significantly widens and deepens laser weld beads. Bead depth and width under different field strengths and orientations are measured. Numerical models are developed to calculate the TEMC current distribution and flow in the melt pool.

The reconstruction of complex soft tissues, such as urethral and nerve tissues, requires constructs that integrate vascularization, lumen integrity, and innervation with clinically relevant mechanical and biophysical properties. Current tissue-engineered tubular constructs often fail due to limited strength, instability under physiological conditions, and insufficient electroactivity. This study demonstrates the unique role of carbon nanofibers (CNFs) in improving the structural fidelity of alginate-gelatin hydrogels for additive manufacturing (3D extrusion printing) and clinical applicability. CNF incorporation improved gel strength, viscoelasticity, printability, and buildability while tailoring stretchability, compressibility, swelling, degradation, antimicrobial activity, vascularization, and inflammatory response in 3D-printed scaffolds. In the 3.5D printing approach, the rapid transformation of flat sheets of the CNF-reinforced hydrogel inks to customized tubular constructs with lumen patency was accomplished. At 0.75% CNF addition, hydrogel inks showed a 1.57- and 2.5-fold improvement in viscoelastic range with respect to shear stress and shear strain and a 1.2-fold increase in elastic recoverability, alongside a 3.6-fold enhancement in fracture stress and a 2-fold increase in elastic modulus under uniaxial tension, and the highest electrical conductivity of 0.5 S/m. Micro-CT confirmed interconnected porous structures with pore volume fraction and pore tortuosity of 0.74 and 1.09, respectively. At the same time, the as-printed tubular grafts (3.8 cm in length, 3 mm internal diameter) exhibited smooth luminal surfaces (∼44-100 nm roughness). NIH-3T3 fibroblasts maintained >80% viability, while antimicrobial analysis revealed strong activity against E. coli and S. aureus. In vivo study in Wistar rats revealed normal regulation of different immune cell markers such as CD8, CD68, TNF-α, COX-2, and IL-6, shifting from acute to chronic inflammation and an ehnacement in vascularization by 30 days as evident from H&E, MTS, and vWF straining. No systemic toxicity in the vital organs was recorded. Collectively, these findings highlight CNF-reinforced alginate-gelatin hydrogels can serve as electroconductive, mechanically robust, and biologically responsive scaffolds with a translational potential for complex soft tissue regeneration.

This paper presents the design and structural analysis of a support system for a 3D printer, developed to improve resin drainage by enabling rotational movement along two axes. Three design variants were created, and after evaluating their performance, the second variant was chosen for its higher torque capacity and potential for future enhancements. This variant showed the most promise in achieving the desired functionality while allowing for further optimization. Finite element analysis (FEA) was utilized to investigate the structural behavior of the support system under loading conditions, ensuring the design remained within the elastic range. The FEA simulations were performed using Beam, Solid, and Shell element types, which provided insights into stress and strain distribution within the structure. This analysis guided the design process, allowing for refinements that improved the structural integrity and load-bearing capacity of the support. Alongside FEA, analytical calculations were performed to assess the bending stress and shear forces on the aluminum profile under three-point bending conditions. These calculations confirmed that the support structure was capable of handling the operational loads while staying within the elastic domain, ensuring reliable performance. This study demonstrates the effectiveness of combining finite element analysis with analytical methods to optimize the design of 3D printer support systems. The results highlight the potential for enhancing the performance and efficiency of additive manufacturing processes through improved structural designs.

Purpose This study aims to enhance the mechanical properties of 3D-printed acrylonitrile butadiene styrene (ABS) by reinforcing it with copper and optimizing key process parameters using Taguchi, artificial neural network (ANN) and metaheuristic optimization techniques. Design/methodology/approach Copper-reinforced ABS filaments were fabricated using a twin-screw extruder and printed via fused deposition modelling. A Taguchi L25 design was used to study the effects of printing temperature and layer height on tensile, compressive and flexural strengths. An ANN was trained on experimental data to model these properties, and a hybrid crow search algorithm–grey wolf optimizer (CSA–GWO) was used for multi-objective parameter optimization. Findings The Taguchi method identified printing temperature as the most influential factor. The ANN model demonstrated high predictive accuracy, achieving R² values exceeding 0.99 and maintaining prediction errors below 2%. The hybrid CSA–GWO algorithm effectively identified optimal parameters for maximizing each mechanical property, with a balanced setting of 245.68°C and 0.100 mm providing strong overall performance (947.97 N tensile, 4,173.61 N compressive and 176.06 N flexural). Originality/value The use of a hybrid CSA–GWO algorithm presents a novel approach within the additive manufacturing domain, offering enhanced exploration and convergence capabilities for optimizing mechanical properties of copper-reinforced ABS composites.

Rationale: Extrusion 3D bioprinting is an additive manufacturing tissue engineering technique that uses cell-laden viscous biomaterials known as bioinks. Manually mixing cell suspensions into viscous biomaterials can be challenging due to the high viscosity ratio between the two fluids. Static mixers are an attractive approach as they can quickly and reproducibly mix two fluids, including those with a high viscosity ratio. However, static mixers intended for viscous applications have not been comprehensively investigated for bioink preparation. This work evaluates the mixing performance, shear stress, and cell viability using four different types of static mixers intended for high viscosity mixing. Methods: Three static mixers intended for mixing viscous solutions were designed based on the Sulzer SMX, Ross ISG, and serpentine mixers and fabricated using resin 3D printing. CELLMIXER, a Kenics-style static mixer commercially available through CELLINK, was used as a comparator. Two biomaterial inks based on PEGDA and methacrylated gelatin were used to characterize each mixer's performance. Shear stress was estimated via fluid dynamics simulations using shear-thinning attributes measured experimentally through rheology. Mixing effectiveness was evaluated using fluorescent beads, from which the most effective design was chosen for live cell mixing experiments. Viability of cell lines (A549 and NIH-3T3) and primary human lung fibroblasts was evaluated postmixing. A demonstration of extrusion bioprinting was performed using the mixed bioinks. Results: The SMX-style mixer provided the most uniform mixing and yielded the lowest simulated shear stresses among the designs investigated. A549, NIH-3T3, and primary human lung fibroblasts maintained viabilities above 96% postmixing using the SMX-style mixer with a more homogeneous cell distribution compared to the CELLMIXER. The bioprinting demonstration validated our mixing system for producing viable tissue constructs with evenly distributed cells. Conclusions: We present a simple, reproducible, and flexible system for mixing cells into viscous biomaterial inks. Our approach facilitates standardized fabrication of cell-laden tissue constructs to ensure consistency in the growing field of extrusion 3D bioprinting.

The emergence of collaborative robotics and additive manufacturing of equipment consumables has had a significant impact on the development of chemical synthesis, biomedicine, the food industry, and agriculture. However, high cost hampers the application of collaborative robots in organic and physical chemistry. Here we suggest a low-cost 3D-printed robotic platform made from gripper and dispenser manipulators coupled with computer vision tools that provide full automation of the Knoevenagel reaction of barbituric acid with aromatic aldehydes, ranging from mixing of reagents to kinetic spectrophotometric monitoring. Screening of conditions of the Knoevenagel reaction between barbituric acid and aromatic aldehydes (reagent ratio, concentration and type of polyelectrolytes and interpolyelectrolyte complexes, as well as type of aromatic aldehyde) powered by the developed open-source Python-based software boosts the discovery of optimal conditions for enhanced reaction kinetics. Our robotic system performs dataset collection and discovers smart polyelectrolyte coacervate catalysis.

Poly(ethylene terephthalate) (PET) is a widely used polymer that accumulates in the environment due to its low degradability, requiring efficient recycling strategies. In this study, CaO filler derived from calcium carbide slag (CS) waste was used for the first time as a catalyst for PET depolymerization. PET/CaO composites were prepared via hot extrusion of PET with the finely dispersed CaO filler. The resulting composite demonstrated consistently higher PET conversion (≥95%) and the yields of dimethyl and dibutyl terephthalates (80 and 84%, respectively). Kinetic studies of glycolysis demonstrated that embedding 1 wt% of CaO in the PET matrix doubled the bis(2-hydroxyethyl) terephthalate (BHET) formation rate relative to an externally added CaO catalyst, which resulted in BHET yields of 84.7% and 41.1% after 40 min. SEM and EDX investigations demonstrated good adhesion between the polymer matrix and the filler. The recovered BHET was successfully re-polymerized to produce recycled PET (r-PET). The maximum rate of weight loss of r-PET samples (at Tmax = 438.7–444.7 °C) was comparable to the original materials (at Tmax = 455.3–457.7 °C). In fact, the direct incorporation of CaO catalyst derived from waste into the polymer matrix during additive manufacturing enabled the implementation of an efficient and scalable closed-loop recycling strategy.

Metal halide perovskites are promising laser light sources due to their exceptional optical gain and solution processability. Structuring the cavity that determines lasing mode and performance, however, is mostly limited to chemical synthesis or in-plane multistep lithographic processes, which lead to high shaping rigidity or poor lasing performance. Here, we introduce a direct electrohydrodynamic three-dimensional printing that produces freestanding, high-performance inorganic perovskite submicro lasers with tailored dimensions and locations, assisted by crystal engineering. The printed vertical nanowires exhibit excellent crystallinity after vapor-phase solvent engineering. Therefore, they show a high-performance two-photon pumped Fabry-Pérot mode vertical lasing with a threshold of 2.98 μJ cm-2, and our on-demand printing method provides the simplest route to tune the lasing characteristics such as lasing threshold and mode spacing, by adjusting the printed nanowire length. We demonstrated that the length-dependent lasing in the printed arrays can configure multilevel anticounterfeiting labels. We expect this additive manufacturing approach combined with crystal engineering to improve the design flexibility and performance of microphotonic circuitries.

Additive manufacturing (AM) is finding increasing application in engineering, especially in manufacturing. As a result, new designs and machines not previously possible due to the restrictions of conventional manufacturing methods may be made. Nevertheless, the same AM parts require post-processing using conventional machining methods such as turning which is the subject of this study. This study provides a comparative analysis of the technological assurance of Ti-6Al-4V parts made via AM using selective laser melting (SLM) and conventional manufacturing methods. The effects of machining parameters such as cutting speed, depth of cut, and feed on the surface roughness of machined Ti-6Al-4V parts are studied. The study concluded that at low feed (0.12 mm/rev.) and low and average depth of cut (0.3 mm and 0.5 mm), the best surface roughness was obtained on the 3D printed samples rather than on the samples obtained using the conventional manufacturing method. In addition, an alternative surface roughness measurement scheme is proposed, which not only allows for measuring the surface roughness, including multiple aspects, but also for identifying possible surface defects in AM parts.

The expanding range of materials available for 3D printing is driving its widespread adoption in advanced fields. As 3D printing becomes increasingly prevalent in the manufacturing of industrial components, its advantages in accommodating complex geometries and reducing material waste are attracting significant attention. Acquiring and applying precise elastic properties of materials during structural design is crucial for ensuring part safety and consistency. However, non-destructive mechanical property assessment methods remain limited. In this paper, we propose an efficient surrogate model, built using a Bayesian model updating approach combined with a random forest algorithm, to achieve high-precision calibration of material elastic constants. In the experiment, samples were 3D printed using fused deposition modeling, and modal information was obtained using operational modal analysis with one end fixed to simulate cantilever beam boundary conditions. Parameter updating was then performed within a Bayesian Markov Chain Monte Carlo framework. The deviation between the updated calculated frequencies and the measured frequencies was significantly reduced, and the Modal Assurance Criterion value between the updated calculated mode shapes and the measured mode shapes was higher than 0.99, demonstrating the accuracy of the updated parameters. Compared to traditional destructive testing methods, the proposed method directly calibrates the structural elastic modulus at the component level without affecting the normal use of the component, providing a more practical approach for the analysis and research of material properties in 3D printing additive manufacturing. The related technology can be extended to other structural forms of 3D-printed products.

Postoperative infection and insufficient osseointegration of orthopedic implants are core challenges leading to surgical failure, and endowing implants with drug storage and release functions has become a key innovative direction to break through this bottleneck. As the core carrier of the drug storage and release system, the size, morphology, and porosity of micro/nano topological structures directly determine the drug-loading efficiency and release kinetics. With its unique advantages of precise controllability and the ability to achieve multi-level topological structure integration in a single step, laser processing technology has received much attention in the integrated application of multifunctional design and drug storage/release for orthopedic implants. This review systematically summarizes the research progress of laser technology in constructing drug storage and release microstructures on the surface of orthopedic implants: first, it introduces the development history of implant surface microstructure design and mainstream preparation methods; then it focuses on the use of ultrafast lasers to construct surface micro/nano topological structures to achieve antibacterial and sustained drug release; it emphasizes the discussion on the preparation of implant scaffolds with complex microstructures and graded porosity by laser additive manufacturing technology, and their application in improving drug-loading capacity and achieving on-demand drug release; finally, it analyzes the existing challenges in this field and looks forward to future development trends and research directions.

The expanding range of materials available for 3D printing is driving its widespread adoption in advanced fields. As 3D printing becomes increasingly prevalent in the manufacturing of industrial components, its advantages in accommodating complex geometries and reducing material waste are attracting significant attention. Acquiring and applying precise elastic properties of materials during structural design is crucial for ensuring part safety and consistency. However, non-destructive mechanical property assessment methods remain limited. In this paper, we propose an efficient surrogate model, built using a Bayesian model updating approach combined with a random forest algorithm, to achieve high-precision calibration of material elastic constants. In the experiment, samples were 3D printed using fused deposition modeling, and modal information was obtained using operational modal analysis with one end fixed to simulate cantilever beam boundary conditions. Parameter updating was then performed within a Bayesian Markov Chain Monte Carlo framework. The deviation between the updated calculated frequencies and the measured frequencies was significantly reduced, and the Modal Assurance Criterion value between the updated calculated mode shapes and the measured mode shapes was higher than 0.99, demonstrating the accuracy of the updated parameters. Compared to traditional destructive testing methods, the proposed method directly calibrates the structural elastic modulus at the component level without affecting the normal use of the component, providing a more practical approach for the analysis and research of material properties in 3D printing additive manufacturing. The related technology can be extended to other structural forms of 3D-printed products.

The objective of the study is to examine the application of additive manufacturing technology in nuclear power plant maintenance, with specific focus on the mechanical properties of the 3D printed parts. The development of additive manufacturing technologies offers a novel opportunity to enhance the efficiency of maintenance processes enabling the rapid, on-site production of required spare parts. These parts can be manufactured based on digital models stored in a database, thereby reducing machine downtime and minimizing storage costs. The literature contains numerous application examples and case studies illustrating the use of additive technology in maintenance contexts. The scope of applicability of the technology is primarily determined by the properties of the printed part. In most cases, redesigning the original part is unavoidable, because additive manufacturing technology is not able to achieve precise tolerances without subsequent machining, and the load-bearing capacity of the parts can also differ significantly from the original part. The mechanical properties of the printed part are significantly influenced by the printing parameters, such as printing orientation, printing temperature, plate temperature, etc.. In this study, we present a series of experiments in which we investigated the effect of printing orientation and printing temperature. Tensile tests were performed on standard test specimens printed with varying parameters.

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM.

Titanium (Ti) and its alloys are widely used in orthopedic applications, including total hip and knee replacements, bone plates, and dental implants, because of their superior biocompatibility, bioactivity, corrosion resistance, and mechanical robustness. These alloys effectively overcome several limitations of conventional metallic implants, such as 316L stainless steel and Co-Cr alloys, particularly with respect to corrosion, fatigue performance, and biological response. However, dense Ti alloys possess a relatively high elastic modulus, which can cause stress shielding in load-bearing applications. This challenge has motivated significant research toward engineered porous Ti structures that exhibit a reduced and bone-matched modulus while preserving adequate mechanical integrity. This review provides a comprehensive examination of powder metallurgy and additive manufacturing approaches used to fabricate porous Ti and Ti-alloy scaffolds, including additive manufacturing and different powder metallurgy techniques. Processing routes are compared in terms of achievable porosity, pore size distribution, microstructural evolution, mechanical properties, and biological outcomes, with emphasis on the relationship between processing parameters, pore architecture, and functional performance. The reported findings indicate that optimized powder-metallurgy techniques can generate interconnected pores in the 100–500 μm range suitable for osseointegration while maintaining compressive strengths of 50–300 MPa, whereas additive manufacturing enables the precise control of hierarchical architectures but requires careful post-processing to remove adhered powder, stabilize microstructures, and ensure corrosion and wear resistance. In addition, this review integrates fundamental aspects of bone biology and bone implant interaction to contextualize the functional requirements of porous Ti scaffolds.

Additive manufacturing (AM) offers precision and efficiency in occlusal splint fabrication; however, the combined influence of build orientation and post-curing duration on the mechanical performance of splint resins remains insufficiently explored. This in vitro experimental study evaluated the effects of three build orientations (0°, 45°, and 90°) and three post-curing protocols (uncured, standard, and extended) on the flexural strength (FS), flexural modulus (FM) and Vickers hardness number (VHN) of a Class IIa biocompatible occlusal splint resin (NextDent Ortho Rigid). A total of 180 specimens were fabricated using a vat polymerization-type 3D printing system. Statistical analyses were conducted using one-way analyses of variance and Tukey’s tests at a significance level of α = 0.05. Both build orientation and post-curing duration significantly affected FS and VHN (p &lt; 0.001). The combination of 45° build orientations and extended post-curing produced the highest FS (169.76 MPa) and FM (7502.17 MPa), exceeding values typically reported for 3D-printed splints, while the 90° orientation with extended curing achieved the highest VHN (21.88). Hardness gains, however, plateaued beyond standard curing, indicating a trade-off between strength and surface hardness. These results demonstrate that print orientation and post-curing time are decisive parameters in optimizing the mechanical performance of 3D-printed occlusal splints. For high-load clinical applications such as bruxism, prioritizing flexural strength over surface hardness may improve appliance longevity, supporting 45° orientation with extended curing as an evidence-based manufacturing approach.

Additive and coating technologies, such as high-velocity oxy-fuel (HVOF) thermal spraying and direct metal laser sintering (DMLS), often require extensive post-processing to meet dimensional and surface quality requirements, which remains challenging for nickel-based superalloys such as Inconel 718. This study presents the design and topology optimisation of a cutting tool with a linear cutting edge, capable of operating in turn-milling or turning modes, offering a viable alternative to conventional grinding. A non-optimised tool served as a baseline for comparison with a topology-optimised variant improving cutting-force distribution and stiffness-to-mass ratio. Finite element analyses and experimental turn-milling trials were performed on DMLS and HVOF Inconel 718 using carbide and CBN inserts. The optimised tool achieved significantly reduced roughness values: for DMLS, Ra decreased from 0.514 ± 0.069 µm to 0.351 ± 0.047 µm, and for HVOF from 0.606 ± 0.069 µm to 0.407 ± 0.069 µm. Rz was similarly improved, decreasing from 4.234 ± 0.343 µm to 3.340 ± 0.439 µm (DMLS) and from 5.349 ± 0.552 µm to 4.521 ± 0.650 µm (HVOF). The lowest measured Ra, 0.146 ± 0.030 µm, was obtained using CBN inserts at the highest tested cutting speed. All improvements were statistically significant (p &lt; 0.005). No measurable tool wear was observed due to the small engagement and the use of a fresh cutting edge for each pass. The resulting surface quality was comparable to grinding and clearly superior to conventional turning. These findings demonstrate that combining topology optimisation with a linear-edge tool provides a practical and efficient finishing approach for additively manufactured and thermally sprayed Inconel 718 components.