Search Results (3,667)

The development of realistic breast phantoms is critical for the evaluation of imaging systems and quantitative image analysis methods. In this work, breast samples derived from the same digital model were produced using 3D printing technology and evaluated for structural similarity and reproducibility. Four independently manufactured phantoms were imaged using mammography and breast tomosynthesis. Radiomic features were extracted from regions of interest in order to assess inter-phantom variability. The results showed very good agreement between the four printed phantoms. Most first-order and GLCM radiomic features exhibited very low inter-phantom variability, indicating consistent structural and intensity characteristics. Neighborhood-based texture features showed slightly higher variability, reflecting their sensitivity to local structural differences. Fractal and power spectrum analyses also confirmed the high structural similarity of the phantoms. These results indicate that the proposed manufacturing approach can produce reproducible breast imaging phantoms suitable for mammography and tomosynthesis imaging studies, with potential applications in imaging system evaluation and radiomic research. Full article

Pelvic organ prolapse (POP) is a common benign gynecological disorder that substantially affects quality of life, particularly in aging female populations. Current management strategies, including standardized vaginal pessaries and synthetic surgical meshes, are often limited by poor anatomical adaptability, mechanical mismatch with native pelvic tissues, and long-term safety concerns. These limitations have driven increasing interest in personalized and biomechanically compatible therapeutic solutions. Three-dimensional (3D) printing, also known as additive manufacturing, has emerged as a promising bioengineering technology to address these unmet clinical needs. By enabling layer-by-layer fabrication directly from digital models, 3D printing allows for precise control over device geometry, mechanical properties, and material composition, facilitating patient-specific design. This narrative review summarizes recent progress in 3D printing for POP management across three major application domains: (i) next-generation meshes based on biodegradable polymers, elastomeric materials, natural biomaterials, and hydrogel systems; (ii) customized vaginal pessaries tailored to individual pelvic anatomy using imaging-assisted workflows; and (iii) imaging-based pelvic models and prototype devices for surgical planning, education, and exploratory assessment. Overall, existing studies demonstrate that 3D printing enables improved biomechanical compatibility, enhanced tissue integration, and multifunctional device design, including drug delivery capability. Although current evidence is largely pre-clinical or based on pilot studies, additive manufacturing holds strong potential to advance POP management toward safer, personalized, and functionally optimized clinical solutions. Full article

Background/Objectives: Accurate quantification of alveolar cleft defects for bone grafting remains difficult due to inconsistent anatomical boundaries. This study established an expert consensus on boundary landmarks for alveolar bone graft (ABG) planning and validated the accuracy and reliability of digital volumetric measurement methods. Methods: Three cleft specialists performed repeated simulated graft procedures in seven patient-specific 3D-printed models, first according to the operator’s clinical judgment, and subsequently according to panel-derived consensus boundaries. Two radiologists independently conducted digital volumetric assessments in 3D X-ray imaging using four measurement approaches (axial tracing, interpolated axial tracing, landmark-based mirroring, and mesh-based mirroring), generating 56 independent digital segmentations to be evaluated against the consensus-based physical reference standard. Volumes of the defects were recorded, intra- and inter-rater reliabilities were calculated using the intraclass correlation coefficient (ICC), and differences among methods were analyzed. Results: Operator-defined plans showed significant inter-operator differences (p < 0.001) with poor-to-excellent reliability (intra-rater ICC 0.060–0.967; inter-rater ICC 0.300–0.635). Consensus established standardized boundaries: tilted plane from base of anterior nasal spine to hard palate, cemento-enamel junctions, incisive canal, and alveolar contour. Consensus-based filling showed non-significant inter-rater differences (p = 0.139) and substantially improved reliability (intra-rater ICC 0.904–0.988; inter-rater ICC 0.622–0.861). Among the four digital methods evaluated, axial tracing demonstrated excellent reliability (intra-rater ICC 0.971–0.99; inter-rater ICC 0.965) and high accuracy (mean difference 0.001–0.026 cm³), with no significant difference (p = 0.999) from the physical reference standard. Conclusions: These proposed consensus-based boundary definitions and validated volumetric measurement methods improved the accuracy and reproducibility of personalized alveolar bone graft planning. Full article

Stability plays essential roles for Vertical Take-Off and Landing (VTOL) vehicles. This paper investigates the stability characteristics of a novel tail-sitter VTOL vehicle employing vector thrust control, specifically focusing on nonlinear modeling and parameter optimization. Firstly, the tail-sitter VTOL which employs vector thrust controlling principles, is designed, and manufactured using 3D printing and carbon-fiber reinforced techniques, with a customized flight controller implemented on the PX4 architecture. To address the nonlinear dynamic characteristics introduced by the vector thrust mechanism, a nonlinear dynamic model for cruise flight is established based on an offline database and validated against cruise flight test data. Flight tests show that the vector-thrust-based pitch control provides rapid response and accurate tracking during cruise flight. Furthermore, based on the validated model, a hybrid optimization strategy combining pattern search and sequential quadratic programming (SQP) is used to tune the cascaded control parameters. Simulation results demonstrate that the optimized controller reduces the rise time from 6.8 s to 0.2 s and the settling time from 10.1 s to 0.9 s under the tested cruise-condition step response, indicating a marked improvement in dynamic response performance. This study provides a practical framework for cruise-flight modeling, pitch-stability analysis, and control-parameter optimization of vector-thrust tail-sitter UAVs. Full article

Fused deposition modeling (FDM)-based three-dimensional (3D) fabrication offers a viable approach to manufacturing highly customized carbon fiber-reinforced polyether ether ketone (CFR-PEEK) components with complex geometries. However, the mechanical properties and surface roughness of FDM-fabricated parts are strongly influenced by processing parameters, particularly layer thickness. This study investigates the influence of layer thickness (0.1 mm and 0.2 mm) on the surface roughness, crystallinity, mechanical properties, and morphological characteristics of FDM-printed 10% CFR-PEEK specimens. The specimens were characterized using mechanical testing, differential scanning calorimetry (DSC), confocal laser microscopy, X-ray micro-computed tomography (µCT), and scanning electron microscopy (SEM). The results show that specimens printed with a 0.2 mm layer thickness exhibit higher crystallinity and ball indentation hardness while also showing increased surface roughness and porosity, with µCT analysis revealing larger and more spatially clustered voids near the sub-perimeter regions. In contrast, specimens printed with a 0.1 mm layer thickness demonstrate higher tensile strength, elastic modulus, elongation at break, and compressive stress. SEM fractography further indicates improved interlayer bonding and a relatively cohesive fracture surface in specimens printed with a 0.1 mm layer thickness. These findings demonstrate clear layer-thickness-dependent processing–structure–property relationships in FDM-printed CFR-PEEK composites and provide guidance for optimizing printing parameters to achieve improved mechanical performance. Full article

Background: Endoscopic sinus surgery (ESS) is a core operative technique in otolaryngology and is associated with a steep learning curve due to complex sinonasal anatomy, limited depth perception with two-dimensional endoscopy, and the requirement for precise bimanual coordination. Given the potential for serious complications, including cerebrospinal fluid leak and visual loss, simulation provides an important opportunity for trainees to develop technical skills in a controlled environment without risk to patients. Recent advances in three-dimensional (3D) printing have enabled the development of high-fidelity models for ESS training. Methods: We describe an evidence-based, graded approach to ESS simulation using two commercially available 3D printed sinus surgery models tailored to the trainee’s stage of training. Early-stage simulation focuses on development of anatomical orientation, endoscopic hand–eye coordination, tissue handling, and basic procedures such as middle meatal antrostomy and anterior ethmoidectomy. Advanced simulation targets more complex procedures, including frontal and sphenoid sinus surgery, transsphenoidal approaches, and management of intraoperative complications. Results: Validation studies demonstrate high face and content validity for both models. Early-stage simulators support acquisition of fundamental technical skills, while advanced models allow simulation of complex anatomy, pathology, and operative complications. Conclusions: A structured, stage-appropriate simulation strategy using high-fidelity 3D printed models may enhance technical skill acquisition and support safe and effective training in endoscopic sinus surgery. Full article

This article presents the experimental characterization of a 3D-printed conformal $2\times 1$ microstrip array antenna designed for 2.4 GHz WiFi backscatter applications in indoor IoT scenarios. Starting from a planar configuration, three conformal states (${30}^{\circ }$, ${60}^{\circ }$, and ${90}^{\circ }$) were realized to systematically evaluate the effect of bending. Detailed simulation and measurement results were obtained in terms of gain, efficiency, and radiation patterns, with the measured gain decreasing from 9.4 dBi in the flat case to 6.2 dBi at ${90}^{\circ }$ bending. To evaluate the system-level impact of these measured gain variations, the measured power levels were incorporated into a TDMA-based WiFi backscatter link model, and the achievable bit transmission rate was assessed under practical indoor conditions, including line-of-sight (LoS), non-line-of-sight (NLoS), and residual interference effects. The main contribution of the work lies in combining the experimental validation of a fully 3D-printed RF-grade conformal antenna with a system-level WiFi backscatter assessment. The combined analytical–experimental results indicate that increasing curvature reduces the achievable maximum bit transmission rate and leads to earlier infeasibility under tighter quality of service (QoS) thresholds within the tested 2.4 GHz indoor WiFi backscatter conditions, suggesting that conformal geometry is an important design consideration for the studied setup. Full article

As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control. Full article

3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the time of shape fixation, the mechanical strength of the structures, and the mechanical environment in which the cells are located immediately after printing. The relationships between bioprinting parameters, material properties, cross-linking strategies, and the presence of cells are highly nonlinear and often investigated through trial and error, leading to significant time and material costs. This paper proposes an approach based on artificial intelligence-assisted simulation, focusing on computer modeling of the biomechanical and functional parameters of hydrogel–cell composites produced by 3D bioprinting. The methodology is based on data generated from computer simulations and allows for analysis of the impact of printing parameters and different cross-linking strategies on mechanical strength, time-dependent geometric stability, and limitations related to cellular function, including exposure time to non-cross-linked matrices. The use of artificial intelligence methods allows for the integration of simulation results and predictive assessment of material behavior, providing a basis for future optimization of bioprinting parameters and process costs prior to experimental validation. Full article

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection models in industrial scenarios. Deep learning-based methods are widely used for metal surface defect detection due to their strong adaptability and high automation. Yet, their existing studies pay limited attention to adaptability, evaluation, and recommendations across different detection methods for metal surface defects. This work mainly discusses YOLO, R-CNN, and transformers, as well as FPN, and analyzes their applications in metal surface defect detection according to their respective characteristics, to provide guidance for future research. YOLO has advantages in real-time industrial online detection, while R-CNN and transformer models show potential advantages in handling complex defect cases. Additionally, this work summarizes commonly used datasets and evaluation metrics for metal surface defect detection and analyzes the benchmark performance of different types of detection methods. It also discusses future research directions, including the current status and improvement paths of different models in terms of accuracy, real-time performance, and adaptability. Future models should focus on balancing accuracy and real-time performance, exploring new hybrid architectures, and improving adaptability to different metal surface defects to support further development in this field. Full article

Hybrid metal–polymer components are used in many industries, such as in aerospace, automotives, and electronics, due to the possibility of reducing the weight of the final part while maintaining mechanical properties comparable to components made entirely of metal. Conventional 3D printing processes do not enable the direct fabrication of hybrid structures consisting of solid metal and polymer parts due to the significant differences in the processing temperatures of both materials. A solution to this problem is the integration of two processes, electrodeposition and photopolymerization, which allow fabrication to be carried out at room temperature. This paper presents preparatory studies aimed at developing a new 3D printing technology that uses the simultaneous application of electrodeposition and photopolymerization to manufacture hybrid metal–polymer elements in a single, integrated 3D printing process. Here, a hybrid metal–polymer element is defined as a component composed of at least two bonded parts, including at least one metal part fabricated by electrodeposition and at least one polymer part produced by photopolymerization. Thus, it is not a polymer component merely coated with an electrodeposited metal layer, but a true hybrid structure consisting of functional metallic and polymeric parts. Such components can be manufactured using the world’s first hybrid 3D printer, which integrates electrodeposition and photopolymerization to produce metal–polymer hybrid parts within a single 3D printing process (the device has been submitted to the Polish Patent Office). However, its design and operating principle are beyond the scope of this paper. The presented research focuses on initial study of selected feedstock materials for this printer, namely photocurable resins and electroplating baths. Since the entire hybrid printing process occurs in an electroplating bath environment, studies of these materials for 3D printing under such conditions were essential. This work includes a screening study of photocurable formulations with respect to rheological properties, 3D printing tests in a model copper electroplating bath, and selection of a suitable bath brightener to maximize the quality (fine grain size, homogeneous grain distribution) of additively deposited copper layers. The study was conducted using copper electrodeposition and acrylate resin photopolymerization as model processes for evaluating the proposed hybrid metal–polymer 3D printing technology. Finally, the most suitable feedstock materials for producing metal–polymer hybrid parts via the proposed 3D printing method were selected. Full article

Accurate needle insertion during epidural anesthesia is challenging due to strong dependence on clinician experience and the limited integration of guidance modalities that simultaneously provide visual feedback and physical motion constraints. Current approaches, including ultrasound guidance and augmented reality visualization, mainly offer passive assistance and do not actively regulate insertion trajectory and depth, which may lead to variability in accuracy and increased risk of complications. This work presents a multimodal human–machine assistance system that combines augmented reality guidance with virtual fixtures to support lumbar epidural needle placement. A Tuohy needle is coupled to a haptic device interacting with a patient-specific L3–L4 lumbar phantom fabricated using 3D printing and ballistic gel. A model-based force profile reproduces the mechanical response of anatomical layers during insertion. Three experimental conditions are evaluated: freehand execution, augmented reality guidance with trajectory and depth visualization, and cooperative guidance using virtual fixtures defined by a cylindrical corridor and a depth-limiting plane. Results show a progressive reduction in mean depth error from 6.82 ± 3.46 mm (freehand) to 4.96 ± 2.41 mm (augmented reality) and 2.21 ± 1.73 mm (virtual fixtures). These findings indicate that the integration of visual and haptic guidance significantly enhances insertion precision and control. The proposed approach highlights the potential of multimodal human–machine cooperation for safer training and assisted interventions. Full article

This study presents a predictive manufacturing framework for customizing the biomechanical response of a 3D printed ergonomic armrest based on relaxed Voronoi metamaterials. A double curved armrest geometry was combined with parametric lattice generation, stereolithography printing in BioMed Elastic 50A resin, uniaxial compression testing of cylindrical lattice specimens, and homogenized finite element simulations using a CT derived forearm model under 15, 30, and 45 N loading. The results showed that both cell size and ligament thickness strongly affected compressive behavior, with smaller cells and thicker ligaments producing higher stiffness and earlier densification. Among the uniform configurations selected for simulation, the E-9-1.5 lattice provided the most balanced response, maintaining contact pressure below about 70 kPa up to 45 N, whereas the stiffer E-7-1.5 configuration exceeded 160 kPa and the E-7-1 configuration surpassed 100 kPa at higher load. Based on these findings, a functionally graded Voronoi concept was developed to combine a more compliant central zone with a stiffer peripheral support region while preserving conformity to the complex armrest boundary. Overall, the results show that relaxed Voronoi lattices offer a computationally efficient route toward anatomically conforming and mechanically tunable cushioning interfaces. Full article

This study investigates the failure mechanisms and machining performance of 3D-printed H13 tool steel end mills driven by the creation of a Finite Element Analysis (FEA)-based digital twin. The primary objective is to assess the process capability of these tools by integrating CAD and FEA with product design simulation-based data acquisition within a digital manufacturing framework, thereby validating a physical model. This research begins by redesigning a 20 mm end mill into a 6 mm, four-flute configuration, and then FEA simulating H13 tool steel and tungsten carbide (WC) tools. This is carried out to machine Al-6082-T6 under spindle speeds of 5500 rpm and 1500 rpm, with a constant feed rate of 0.5 mm/tooth over 100,000 cycles. The process is integrated with the Siemens Insights hub via Node-RED to replicate the simulation to correlate the CPU signal spikes and enhanced processing capacity, especially in relation to CAD/CAE kernel activities. Based on the simulation insights, two H13 end mills are fabricated using Fused Filament Fabrication (FFF). The first tool, tested at 5500 rpm and a 1100 mm/min feed rate, fractured after 70 mm of cutting. The second trial, using a diamond-coated solid carbide tool at 1500 rpm and 300 mm/min, achieved successful machining with graphene-enhanced coolant. The cutting forces ranged from 300 to 500 N for 3D-printed tools, compared with 150–180 N for the carbide tool. The surface roughness varied between 0.6–1 µm and 4–6 µm for the printed tools, aligning closely with traditional tools (0.5–1 µm). Post-machining analysis using SEM and EDX confirmed tool wear and material changes. This work adopted a methodology to capture and monitor CPU signal spikes via the digital twin platform, enabling real-time comparison with failure thresholds. The results demonstrate the feasibility of using 3D-printed H13 tools for sustainable, customizable machining, offering a pathway for industries to adopt in-house tool design and manufacturing with integrated digital validation. Full article

Planar honeycomb structures, especially biomimetic hexagonal honeycombs, are widely used in energy-absorbing equipment because of their excellent out-of-plane deformation resistance. However, their significant mechanical anisotropy, manifested by the large discrepancy between out-of-plane and in-plane responses, greatly restricts their broader applications. Inspired by spiral-reinforced thin-walled biological tubular systems, such as animal tracheae and plant vessels, this study proposes a biomimetic reinforcement strategy by embedding spiral structures along the thin walls of planar honeycombs. To validate the feasibility of the proposed design, biomimetic honeycomb specimens were fabricated using 3D-printing technology and tested under compression along different loading directions. Furthermore, a numerical model validated against the experiments was developed to reveal the underlying enhancement mechanism. The results demonstrate that the proposed biomimetic honeycomb preserves the favorable out-of-plane performance of the conventional hexagonal honeycomb, while improving the in-plane energy absorption capacity by up to 70%. The biomimetic spiral reinforcements enable more effective load transfer under multidirectional loading, resulting in a more uniform plastic stress distribution over the entire structure and activating a larger deformation region for energy dissipation. The present work provides a bioinspired strategy for developing lightweight energy-absorbing structures for potential applications in aerospace, rail vehicles, marine engineering, and civil structures. Full article