Additive manufacturing

This experimental research investigates the structural performance of novel 3D-printed multilayer sandwich structures with bio-inspired cellular core configurations under quasi-static out-of-plane compressive load. Specimens were fabricated using the Multi Jet Fusion (MJF) additive manufacturing method with Nylon PA12 and designed with various core geometric configurations to assess their impact on mechanical performance and energy absorption capacity. The experimental results indicate that structural geometry significantly influences the energy absorption efficiency of the proposed novel multilayer sandwich panels. Among the tested configurations, the sample with the rhombic cell shape proved to be the most effective. They distributed stress evenly and deformed gradually, preventing sudden failure or buckling in the core components. Hexagonal cell structures also exhibited significant energy absorption properties, providing an optimal balance between structural strength and weight efficiency. In contrast, square and circular cell shapes were less effective than other cell types, exhibiting lower load-bearing capacity and reduced energy absorption efficiency. These findings demonstrate the potential of the proposed 3D-printed bio-inspired structures to advance the development of lightweight, high-performance solutions for a wide range of engineering applications.

Keywords: Additive manufacturing , Bio-inspired structure , Compressive behavior , Multi Jet Fusion (MJF) , Multilayer sandwich panel

Cutting guides are widely used in cranio-maxillofacial surgery by providing mechanical references for precise bone resections and reducing intraoperative variability. Nevertheless, their rigid and patient-specific design requires dedicated CAD modeling and fabrication, making them time-consuming to produce and difficult to adapt when anatomical conditions or bone surfaces change. This work presents a hybrid surgical cutting guide that combines a physically adjustable device with Augmented Reality (AR) feedback to support intraoperative alignment in maxillofacial osteotomies. The concept merges the tactile reliability of conventional guides with the adaptability of digital visualization, enabling surgeons to fine-tune the cutting plane directly through AR tracking. Registration was performed using cephalometric landmarks and an inside-out tracking approach with HoloLens 2, allowing precise superimposition of virtual cutting planes onto 3D-printed mandibular models. The system was evaluated by both expert and novice operators under three feedback conditions: no AR, holographic overlay, and real-time distance guidance. Results showed that AR feedback was associated with improved positional accuracy, with mean linear deviations of 1.27±0.71mm and angular errors of 4.46±3.27°. Operator experience influenced overall performance, yet enhanced feedback compensated part of this variability. Combining physical and digital guidance can yield more adaptable, precise, and reusable osteotomy tools, paving the way for flexible surgical assistance in clinical settings.

Keywords: Adjustable surgical guide , Augmented Reality , Cranio-maxillofacial surgery , Hololens 2 , Hybrid surgical guide , Pose estimation

Additive Manufacturing (AM) is being explored as a promising solution for producing complex components in nuclear fusion plants, which are still under development. Traditional manufacturing struggles with the intricate geometries and customization required for these parts, making AM an attractive alternative due to its design flexibility and cost-effectiveness. This paper investigates the potential of AM—specifically Laser Powder Bed Fusion (L-PBF)—in fabricating the Outboard Target Body (OTB) of the DEMO divertor, a key component in controlling plasma impurities and heat. The OTB must meet strict technical and functional requirements, and AM offers the possibility to optimize its design while integrating advanced cooling channels and complex shapes that are difficult to achieve with conventional methods. The study evaluates the structural performance and fluid dynamics of the OTB prototype, considering how AM can influence design improvements and enable more efficient production. Overall, AM represents a critical innovation in advancing fusion reactor component manufacturing.

Keywords: Additive Manufacturing , DEMO , L-PBF

Tracking systems are essential in various fields, such as health and manufacturing industries, enabling mapping between the real and digital worlds. Amongst others, Augmented Reality Tracking Systems (ARTS) are more recent and less explored. This work proposes a quantitative metrological methodology to evaluate ARTS tooltip tracking performance, facilitating benchmarking, parameter optimization, and system selection for specific tasks. A specific 3D-printed measuring artifact is proposed to guide tooltip positioning. Tracking accuracy and precision are estimated, highlighting the effects of influence factors. The methodology was tested with two commercial state-of-the-art ARTSs using marker-based tooltips, i.e., a Microsoft HoloLens 2 and a stereo camera system equipped with Intel RealSense SR305 cameras. Metrological characteristics are evaluated, and the Euclidean distance expanded uncertainty at a conventional 95% confidence level is estimated as 5.071mm for the HoloLens 2 and 6.800mm for the stereo system, resulting in a superior metrological performance of HoloLens 2 under the specified conditions. This study provides a standardized approach for quantitatively comparing AR tracking systems, offering valuable insights for optimizing their use in specific applications and, innovatively in the context of ARTS, associates measurement uncertainty with tracked distance values.

Keywords: Augmented reality , Design of experiments , Measurement uncertainty , Metrological characteristics , Tooltip tracking

Background: In recent years, the growing adoption of Virtual Reality (VR) and 3D printing technologies has revolutionized surgical training by providing innovative opportunities for hands-on education. This study investigates the combined use of VR and 3D printed personalized anatomical models and cutting guides within the field of oral and maxillofacial oncologic surgery. Materials and methods: A mandibular tumour case was developed using the proposed approach, integrating both virtual and physical tools. Feedback was gathered from twelve surgical residents regarding their understanding of the case, the effectiveness of the immersive and three-dimensional technologies, and their overall satisfaction with the training experience. Results: Participants reported enhanced comprehension of complex surgical scenarios and valued the practical utility of the VR simulator combined with 3D printed models. The immersive environment facilitated skill acquisition in a risk-free setting. Conclusion: The findings underscore the significant added value of integrating VR and 3D printing technologies in surgical training, preparation, and simulation. This approach offers a safe, effective training platform that improves readiness for complex procedures in oncologic surgery and has the potential to be extended to other branches of maxillofacial surgery.

Keywords: fibula free flap , mandibular reconstruction , maxillofacial surgery , surgical education , three-dimensional printing , user-centered design , virtual reality

Background: Pediatric surgical tracheotomy is a high-risk procedure demanding specialized training due to the unique anatomical and physiological characteristics of children. A key challenge in surgical education is the limited availability of high-fidelity simulators that provide a safe environment for skill development. Methods: We developed an innovative pediatric task trainer for surgical tracheotomy using 3D printing and silicone casting. This simulator features a modular design with a replaceable cartridge replicating critical anatomical structures, including the trachea, thyroid gland, cervical fascia, vessels, and musculature. To enhance realism, it also simulates bleeding upon incision. Results: Expert physicians evaluated the trainer's anatomical accuracy, procedural realism, and overall training effectiveness. Feedback was highly positive, confirming that the device closely replicates pediatric tracheotomy conditions and offers a realistic training experience. Conclusion: The positive expert feedback highlights the simulator's potential as a valuable tool for improving surgical proficiency and reducing the risk of complications in live procedures. This task trainer represents a significant advancement in pediatric airway management training and an essential asset for surgical simulation.

Keywords: 3D printing , CAD modeling , Clinical simulation , Pediatric task trainer , Surgical tracheotomy

Novel injection moulding tools have been developed using metal additive manufacturing, particularly Selective Laser Melting (SLM). This technique enables the fabrication of new complex geometries, including the integration of lattice structures within components. These structures are renowned for their lightweight and high-strength characteristics. When designed with enhanced thermal properties, lattice structures have the potential to significantly improve the performance of injection moulding tools, where efficient thermal management is of importance. To realise these innovative thermal capabilities, a comprehensive investigation of the thermal behaviour and conductivity of the structures is essential. To this end, a cost-effective experimental setup has been designed and constructed. The system employs a comparative method, whereby heat flow through a 3D-printed sample is measured in series with a reference material. By analysing the temperature gradients across both bodies, the thermal conductivity of the printed structure can be accurately determined. BK7 glass is utilised as the reference material because of its well-characterised and stable thermal conductivity. A key factor affecting measurement accuracy is interfacial thermal resistance, which arises at the contact interface between two materials and can hinder heat transfer. This resistance is influenced by the material properties, surface finish and contact pressure. To minimize the effect of interfacial resistance and ensure more reliable conductivity measurements, multiple tests are conducted on the same structure under varying temperature conditions. This approach facilitates the identification and compensation of thermal contact resistances.

Keywords: Injection , Lattice Structures , Low-Cost , Moulding , Selective Laser Melting , Thermal Conductivity

Multi-material additive manufacturing (MMAM) enables integration of multiple materials within single products, but existing design methodologies lack systematic frameworks linking detailed consolidation decisions to product-level functional requirements while preserving functional independence. This paper presents a methodology that extends the conventional design process model with a reverse-traced workflow connecting part-level decisions to higher-level product architecture. By tracing how Design for MMAM (DfMMAM) affects design decisions in reverse, designers can identify the best opportunities to use MMAM based on their project scope. The methodology introduces a Level of Process Integration (LPI) framework based on design novelty that structures redesign scope according to whether changes affect part geometry, component assembly, or function allocation, enabling designers to balance consolidation benefits against validation complexity at each level. Sequential decision-making workflows systematically determine which functions can be co-located within unified components while maintaining functional independence through zone-specific design parameters. The methodology is illustrated through a qualitative case study on trail running shoe design across three integration levels, identifying substantial consolidation potential while establishing the foundation for future quantitative validation. Unlike existing approaches limited to part-level redesign, this framework traces detailed consolidation decisions back to product architecture trade-offs, clarifying redesign scope and validation rigor required at each integration level.

Keywords: Design for MMAM (DfMMAM) , function combination , function sharing , Level of Process Integration (LPI) , Multi-Material Additive Manufacturing (MMAM) , part consolidation , product architecture

This paper introduces a numerical framework that models the representative volume element (RVE) of lattice structures made of viscoelastic material to investigate the influence of different RVE topologies on their macroscopic elastic behavior and damping capability. Specifically, these properties are obtained through an energy-based homogenization procedure, while the viscoelastic behavior of the bulk material is described using the Bagley–Torvik fractional derivative model, which describes its frequency-dependent response. A comprehensive numerical campaign is carried out on eleven lattice topologies across a wide range of relative densities and excitation frequencies. Results reveal that, for lattice structures made of a single material phase and the void phase, the macroscopic elastic response is essentially governed by the RVE topology and its relative density. Furthermore, the dependence of equivalent elastic properties on the frequency at the macroscopic scale is consistent with that of the bulk material at lower scale. Finally, despite each RVE topology exhibits a certain elastic symmetry, the loss factors matrix at the macroscopic scale does not depend on the RVE topology and is identical to that of the bulk material. Consequently, the macroscopic complex stiffness tensor exhibits the same frequency dependence as the bulk viscoelastic material, enabling homogenization at arbitrary frequencies.

Keywords: additive manufacturing , frequency-dependent behavior , homogenization , Lattice structures , viscoelasticity

Origami-inspired design and additive manufacturing enable the rapid fabrication of compact parallel mechanisms with integrated compliance and reduced assembly complexity. This article presents OriDelta, an origami-inspired Delta robot (137 × 158 × 84 mm) fabricated via multimaterial extrusion additive manufacturing with compliant flexural joints. Experimental validation using optical motion capture revealed a systematic, configuration-dependent platform tilt arising from the interaction between mechanism geometry and flexure joint compliance. This parasitic motion exhibits a strong linear correlation with platform position and directly contributes to the positioning error. Exploiting this predictability, we develop a two-stage feedforward compensation model to predict tilt from commanded positions and correct the resulting position error. The proposed approach reduced the rms positioning error to 1.21 ± 0.53 mm, corresponding to an average improvement of 50.2% in kinematic accuracy across all trajectories and protocols. These results demonstrate the effectiveness of model-based compensation in compliant origami mechanisms and provide quantitative insight into the role of compliance-induced parasitic motion in origami-inspired parallel manipulators.

Keywords: Compliant hinges , kinematic model , origami design , parallel robot

Additive manufacturing (AM) enables customized, efficient restorative workflows, though the accuracy of 3D-printed restorations may be compromised by polymerization, sintering shrinkage, and post-processing. This study evaluated the geometric accuracy of 3D-printed partial restorations compared with the computer-aided design (CAD) reference. The null hypothesis stated that no significant differences would be found between Varseo Smile Crownplus (by BEGO, Italy) and IRIXMax (by DWS System, Italy) materials, which are printed and cured with different technologies. A model was prepared for an overlay and designed with a 1.5 mm uniform thickness. Restorations were produced in two groups with two different printing processes: DLP (digital light processing)-printed Varseo Smile Crownplus and SLA (stereolithography)-printed IRIXMax. Six samples per group were printed at 90° orientation and scanned. Meshes were aligned to the master geometry via pre-alignment and ICP (Iterative Closest Point) registration. Deviations were quantified in CloudCompare using mean, standard deviation (SD), and 90th percentile values. IRIXMax showed the lowest deviations from the ideal geometry, while Varseo Smile Crownplus exhibited greater variability. Pairwise comparisons found IRIXMax significantly more accurate than Varseo Smile Crownplus. Color maps confirmed material-specific deviation patterns. IRIXMax provided the highest geometric accuracy. Material-specific calibration is essential for reliable 3D-printed definitive restorations.

Keywords: 3D printing , dental restorations , deviation analysis geometric accuracy , internal gap , marginal fit , resin-based composites , trueness

Currently, Additive Manufacturing is revolutionizing the production of complex and customized components across various industries, offering significant advantages in material efficiency, design flexibility, and rapid prototyping. Concurrently, Machine Learning models have become increasingly crucial in Additive Manufacturing, improving decision-making, process efficiency, predictive accuracy, and defect recognition. However, the practical implementation of Machine Learning models in production environments presents significant challenges, such as managing incremental knowledge without causing catastrophic forgetting of old knowledge. The complexity of the additive process and the large number of parameters often require training the model on new data without forgetting previously acquired knowledge. Continual Learning is an emerging practice for incremental knowledge management in Artificial Intelligence. This paper presents a novel Continual Learning approach for class-incremental learning tasks, called Progressive Online Ridge Regression (PORR), based on an extended version of Ridge Regression that fine-tunes a pre-trained Convolutional Neural Network, MobileNetV3. The method is applied to an image analysis problem to recognize defects in Powder Bed Fusion of Polymers. An open-access dataset has been analyzed to validate the approach. The experimental results demonstrate that the proposed approach reduces catastrophic forgetting by optimizing computational resource allocation with respect to accuracy, training time, CPU utilization, and maximum RAM usage.

Keywords: Additive manufacturing , Continual learning , Defect recognition , Machine learning , Powder bed fusion of polymers

This study presents a systematic evaluation of the dimensional accuracy and process capability of fused deposition modeling (FDM) additive manufacturing, with emphasis on the influence of process-induced thermal and kinematic effects on dimensional performance. Two geometries made of polylactic acid (PLA) were considered: a staircase specimen to assess process capability and directional effects, and a geometric benchmark test artifact (GBTA) to evaluate dimensional reliability across representative features. Measurements were performed using a digital caliper for the staircase model and a coordinate measuring machine (CMM) for the GBTA. Under manufacturer-recommended, unoptimized printing conditions, staircase analysis showed that the investigated FDM system achieved process performance indices (Pm and Pmk) ≥ 1.33 at a 95% confidence level. Dimensional stability varied across the build chamber due to spatially nonuniform cooling conditions, with statistically significant position-dependent deviations observed particularly along the build direction. Larger features exhibited higher relative precision. Achievable ISO 286-1 International Tolerance grades ranged from IT9 to IT14. GBTA results confirmed systematic undersizing of cylindrical and curved features. Absolute dimensional deviations remained approximately constant across nominal sizes, whereas percentage errors increased for smaller features due to scale effects. Despite these variations, the geometric form was preserved, demonstrating high repeatability. The proposed capability-based evaluation framework provides a structured approach to quantifying dimensional reliability in polymer-based material extrusion processes for functional components.

Keywords: additive manufacturing , dimensional accuracy , dimensional tolerances , fused deposition modeling , process capability

The Fused Deposition Modeling (FDM) process often produces parts with high surface roughness, limiting their end-use applications, especially in the biomedical field. This paper presents an experimental study on improving the surface finish of 3D-printed polycaprolactone (PCL) samples using a robotic burnishing process. A key innovation is the development of a low-cost sensorless setup using a 5-DOF manipulator, which controls the applied force by correlating a precise robotic displacement with the known stiffness of springs via Hooke's law. Ten PCL samples were tested using two burnishing directions: 90° (perpendicular) and 0° (parallel) relative to the printing orientation. The as-printed samples showed a highly anisotropic surface. The 90° trajectory (group 1) proved to be more effective in reducing primary roughness ((Formula presented.)), lowering the mean (Formula presented.) from (Formula presented.) to (Formula presented.) (a mean reduction of 29.9%). In contrast, the 0° trajectory (group 2) was more effective in reducing roughness (Formula presented.), lowering its mean (Formula presented.) from (Formula presented.) to (Formula presented.) (a mean reduction of 34.0%). The results demonstrate that the proposed sensorless system is a valid method for surface post-processing of FDM parts when the required forces fall below a specific threshold, ensuring a significant reduction in roughness without damaging the samples. The lower surface roughness obtained with the proposed post-processing strategy may represent a promising approach for improving the surface characteristics of FDM-fabricated polymer scaffolds intended for biomedical applications.

Keywords: fused deposition modeling , indirect measurement , robotic burnishing

Additive Manufacturing enables the production of complex and customized structures but achieving consistent geometric fidelity and dimensional control remains challenging. Variations in form and feature orientation can compromise functional performance, making it essential to understand how process and material parameters affect geometric deviations and support robust qualification. This research investigates the effects of infill density, scale, and material type on geometric variations in Fused Filament Fabrication parts. Two shapes (prismatic and solid-of-revolution) were designed, with geometric variations grouped by feature orientation (vertical, horizontal, and inclined). The experimental plan considers three infill density levels (25%, 50%, 75%), three scale factors (1:1, 1.5:1, 2:1), and four materials (PET-G, PET-G CF, Nylon, Nylon CF), for a total of 120 samples, whose actual geometries were reconstructed via laser scanning, providing the basis for evaluations of data through numerical analysis. The results revealed that input parameters affect geometric accuracy, negatively impacting inclined features with increasing scale, while improving some horizontal features with increasing infill density. Materials, particularly carbon fiber reinforced, help reduce deviations, particularly in vertical geometric features. The study emphasizes the importance of selecting appropriate input variable settings and material types to achieve the desired accuracy, providing a tolerance catalogue within a specified range for designers and engineers to define tolerances for 3D-printed parts.

Keywords: Experimental plan , Fiber reinforced polymers , Fused filament fabrication , Geometric dimensioning and tolerancing , Tolerance catalogue

This study proposes, validates, and optimizes an innovative recoverable composite energy absorber based on a foam-filled hexachiral auxetic architecture made of a visco-hyperelastic polymer. The absorber combined a 3D-printed hexachiral frame made of thermoplastic polyurethane reinforced by waste tire rubber (TPU-WTR) and a strain-rate-sensitive highly compressible open-cell polyurethane foam. While the combination of frame auxeticity with foam filling enhanced localized energy absorption, the specific materials adopted guaranteed full recoverability after impact and the variation of geometrical parameters in the hexachiral topology provided a significant design flexibility. A comprehensive mechanical characterization was performed, based on static and dynamic tests, which were used to calibrate accurately their numerical models. An absorber prototype was produced and experimentally tested, assessing the full recoverability at different impact velocities. Finite element models of the absorber were developed by using the identified material models and were validated from the quantitative and qualitative standpoint. Leveraging the design flexibility of the concept, fully parametric models were used to optimize a configuration for an impact of a 12 kg mass at 9 m/s, with a force constraint representative of the limit referred to a collision between a vehicle bumper and a pedestrian leg. The exploited Gaussian process regression (GPR) surrogate models, combined with a genetic algorithm, were able to predict the largely variable performance indices of the different configurations and enabled the identification of an optimal solution that significantly minimized the indentation of the impactor, while satisfying the force constraint and preserving full recoverability.

Keywords: Energy absorption , Finite element modelling , Hexachiral auxetic structure , Optimization technique , Recoverable absorbers , Surrogate model

Binder Jetting is an innovative Additive Manufacturing process that was employed to produce 17-4PH steel components for orthopedic surgery applications, starting from pre-alloyed powders. Poor surface finishing of as-printed components often requires post-processing to comply with biomedical requirements. Mass finishing is a widely applied technology to lower roughness by means of mechanical abrasion. This study optimized a centrifugal disk machine mass finishing process for 17-4PH components produced by Binder Jetting. Surface printing orientation, abrasive paste composition, and precipitation hardening thermal treatment effects were investigated. Sample morphology was characterized by optical and scanning electron microscopy, roughness was measured by contact and confocal profilometry, material removal rate was estimated by mass loss measurements, and surface chemical composition was investigated by energy-dispersive X-ray spectroscopy. SiC, Al2O3, and SiO2 containing abrasive pastes effectively reduced roughness across all printing orientations. Abrasive smoothening effectiveness was correlated with the material removal rate. SiC-based paste resulted in the highest roughness reduction, reaching an average Ra of 0.62 ± 0.25 μm after 4 h of treatment. Traces of mass finishing abrasive pastes were observed on the polished surfaces, not precluding biomedical compatibility for orthopedic surgical applications. Upon precipitation hardening treatment, Vickers hardness increased by 42%. As a consequence, the material removal rate and roughness reduction were slowed down. Experimental results indicated that performing the mass finishing process before thermal aging allows roughness requirements to be met in a shorter time.

Keywords: Additive manufacturing , Binder jetting , Mass finishing , Surgical instruments

In this research, novel bio-inspired multilayer sandwich panels were fabricated by Multi Jet fusion and evaluated under quasi-static out-of-plane compression to examine the influence of core topology, cell size, intercell spacing, and layer orientation. The rhombic and hexagonal cores exhibited superior performance, with absorbed energies of 440.46 J and 363.96 J, respectively, and specific absorbed energies (SAE) of 4.50 J/g and 4.18 J/g. In contrast, the square and circular cores absorbed less energy, 264 J and 270 J, with SAE of 3.83 J/g and 3.60 J/g, respectively. Reducing the inscribed diameter from 2.0 to 1.5 mm increased peak load by between 10 and 15%, while energy absorption rose by 13% in circular cores and 6% in hexagonal cores, with SAE gains of 10 and 5%, respectively. Increasing intercell spacing from 2.0 to 2.5 mm improved energy absorption by 12% in circular cores and 14% in hexagonal cores, with SAE increases of 5 and 7%. Alternating corrugation orientation further enhanced performance, raising energy absorption by 20% in circular cores and 22% in hexagonal cores. In general, energy absorption was governed by progressive failure mechanisms including yielding, buckling, delamination, and cracking. These insights bridge additive manufacturing and bio-inspired design with structural engineering by linking core topology and geometry to measurable gains in peak load, absorbed energy, and specific absorbed energy.

Keywords: Additive Manufacturing , Bio-inspired structures , Corrugated core configurations , Multi Jet Fusion (MJF) , Multi-layer sandwich panels

This study investigates the mechanical performance of novel bio-inspired sandwich structures subjected to quasi-static out-of-plane compression. Lightweight corrugated cores were fabricated via Multi Jet Fusion (MJF) using PA12 reinforced with 40 wt% glass beads. The design was inspired by the natural geometry of oak tree cupules and balsa wood face sheets. A comprehensive experimental campaign examined the influence of key geometric parameters, corrugation height, diameter, and wall thickness, on compressive behavior. All bio-inspired specimens significantly outperformed the bio-based reference panel (BioCom) in terms of maximum compressive load (MCL), absorbed energy (E), and specific absorbed energy (SAE). The best-performing design, Bio-Ins 1, exhibited a fourfold increase in MCL and an eightfold increase in energy absorption, with SAE more than doubled relative to BioCom. Increasing corrugation height led to a 75% gain in peak load and a twofold increase in SAE. Enlarging core diameter doubled the peak load and improved total energy absorption by a factor of 2.75, while increased wall thickness contributed an additional 10% rise in load capacity and a 36% increase in energy absorption. Failure analysis identified a five-stage progressive deformation sequence: initial delamination and micro-cracking, plastic buckling with radial bulging, mixed-mode cracking, extensive core crushing, and final fragmentation. These results demonstrate that mechanical response is governed primarily by architectural parameters rather than material composition. This work highlights the effectiveness of geometry-driven design in developing energy-absorbing, lightweight sandwich panels for advanced structural applications.

Keywords: Additive manufacturing , Bio-inspired design , Composite sandwich structure , Lightweight design , Multi Jet Fusion

Effective pediatric airway management, particularly in patients with congenital anomalies, is a significant clinical challenge, complicated by the scarcity of realistic pathological training models. This study describes the design, development, and validation of a high-fidelity pediatric difficult airway simulator for training in rigid and flexible bronchoscopy. Developed by the T3Ddy Laboratory with the University of Florence and Meyer Children\'s Hospital—IRCCS, the mannequin was based on CT data from a 19-month-old patient with complex airway anomalies, using reverse engineering and additive manufacturing. The simulator adopts a modular design with interchangeable anatomical components, enabling replication of diverse clinical scenarios, including rare congenital tracheal conditions. Key features include a flexible cervical spine for dynamic head positioning, simulated lubrication and hemorrhage systems, and anatomically precise upper airway structures. The airway underwent iterative redesign to balance anatomical fidelity and manufacturability. Production combined fused deposition modeling with silicone casting in custom molds incorporating functional details. Validation occurred during a pediatric bronchoscopy training course, where structured questionnaires assessed realism, usability, and educational value. Participants reported high satisfaction, particularly with rigid bronchoscopy, while suggesting improvements in haptic feedback and fluid simulation. This work demonstrates that advanced design and manufacturing can create customizable, reusable simulators that strengthen pediatric airway management training and fill critical gaps in pathological model availability.

Keywords: 3D printing , clinical simulation , high-fidelity medical training , pediatric airway management