Search Results (1,854)

Introduction: 3D-printing is an emerging technology in the field of prosthetics, offering advantages such as cost-effectiveness, ease of customization, and improved accessibility. While previous reviews have focused on limited aspects, the aim of this systematic review is to provide a comprehensive evaluation of the clinical outcomes of 3D-printed prostheses for both upper and lower limbs. Methods: A search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across six databases (PubMed, Web of Science, EBSCO, Scopus, Cochrane Library, and Sage). Studies on 3D-printed prostheses in human rehabilitation that focused on the clinical outcomes of the device were included, while studies lacking clinical data, 3D printing details, or focusing on traditional manufacturing methods were excluded. Finally, the risk of bias was assessed using the modified Downs & Black Checklist. Results: A total of 1420 studies were identified, with 11 meeting the inclusion criteria. The included studies assessed different 3D-printed prosthetic types and upper and lower limb prostheses. The main clinical outcomes analyzed were functional performance, design and material integrity, and overall effectiveness of 3D-printed prostheses. Studies on upper limb prostheses reported improved dexterity, range of motion (ROM), and user satisfaction, despite some durability limitations. Lower limb prostheses showed enhancements in comfort, gait parameters, and customization, particularly in amphibious and partial foot designs. Conclusions: 3D-printed prostheses show potential to improve functional performance, patient satisfaction, fit, and implementation feasibility compared to conventional methods. However, limitations such as small sample sizes, variability in assessment tools, and limited high-quality evidence highlight the need for further research to support broader clinical adoption. Full article

Reducing the carbon footprint of the construction sector is a growing priority. This study explores the potential of using submerged arc welding (SAW) slag as a precursor in the development of low-carbon geopolymeric materials for 3D printing. The influence of potassium hydroxide (KOH) molarity, partial replacement of ground granulated blast furnace slag (GGBFS) with SAW slag, and water-to-binder (w/b) ratio was evaluated in terms of fresh and hardened properties. Increasing KOH molarity delayed setting times, with the longest delays at 10 M and 12 M. The highest compressive strength (48.5 MPa at 28 days) was achieved at 8 M; higher molarities led to strength losses due to excessive precursor dissolution and increased porosity. GGBFS replacement increased setting times due to its higher Al₂O₃ and MgO content, which slowed geopolymerization. The optimized formulation, containing 20% SAW slag and activated with 8 M KOH at a w/b ratio of 0.29, exhibited good workability, extrudability, and shape retention. This mixture also performed best in 3D printing trials, strong layer adhesion and no segregation, although minor edge irregularities were observed. These results suggest that SAW slag is a promising sustainable material showing for 3D-printed geopolymers, with further optimization of printing parameters needed to enhance surface quality. Full article

Various industrial technologies require flexible material webs to undergo processes such as thermal treatment (e.g., drying), printing, or laminating. Such processes are usually performed within winding systems, where the web goes through a set of rolls, and the precision of the web movement determines the quality of the final product. Therefore, high accuracy in the control of both the longitudinal and lateral movement of the web is of paramount importance. Designing the proper control system requires insightful analysis of the technological setup and precise modeling of its dynamic properties. In this paper, the transfer function model of the roll-to-roll system with closed-loop web circulation has been developed based on the mathematical description of the open-loop system. It has been proven that the analyzed system can be efficiently represented by an integral block with negligible inertia. Having established this, several control algorithms have been analyzed, and, as a result, the dedicated adaptive–predictive control algorithm has been proposed. The developed solutions have been verified both by simulations and real experiments performed using the semi-industrial laboratory setup. The high control quality of the proposed algorithm (e.g., considerable reductions in overshoot and settling time compared to PI control), outperforming classical approaches, has been confirmed under various disturbances. Full article

Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (${\lambda }_{\mathrm{stat}}$ of 669, ${\lambda }_{\mathrm{dyn}}$ of 630), whereas the two-strip design produced the highest signal quality (SNR_stat = 9.5 dB, SNR_dyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects. Full article

Three-dimensional printing has enabled the production of complex parts that are difficult to create with conventional manufacturing methods. Its additive nature has made it possible to create interconnected (assembled) parts in a single manufacturing step. This requires the development of new ways of designing, manufacturing, and testing mechanisms that do not require assembly after their creation, called non-assembly mechanisms. An approach is proposed for the design and experimental study of the properties of rotational joints created already assembled using FFF technology for 3D printing. The advantages and disadvantages of different 3D printing methods that can be used to obtain such assemblies are discussed. Basic principles for the design of assembled rotational joints, built without support structures, are introduced. Two examples of their application in creating functional robot models are presented. The features during production, and the advantages and disadvantages of the models are discussed. Models of directly assembled rotational joints with different clearances are studied, and an experiment is conducted based on measuring the magnitude of the current during the rotation of a link. This provides indirect results for the rolling resistance, on the basis of which the qualities of the joint are judged. The results from the experiments show that rotational joints with a diameter d = 10 [mm], created using FFF technology and PLA material, have the lowest resistance at a clearance in the range t = 0.15–0.25 [mm]. Full article

This study aimed to evaluate the scientific reliability of 3D-printed silicone boluses fabricated with patient-specific molds, focusing on fabrication-related uncertainties such as internal air bubbles, thickness variations, and density differences, thereby providing evidence for clinical quality assurance. Custom silicone boluses were fabricated using 3D-printed molds with varying vacuum degassing times (1, 5, and 10 min). Air bubble size and depth were quantified using scanner image analysis, while density and Hounsfield unit (HU) values were compared with a commercial bolus. Dosimetric evaluation was performed using a VitalBeam linear accelerator (6 MV photons, Varian Medical Systems, Palo Alto, CA, USA) and a MatriXX 2D detector (IBA Dosimetry, Schwarzenbruck, Germany), comparing treatment planning system (TPS) calculated doses with measured doses across a 3 × 3 grid. Surface dose distributions were further analyzed using EBT3 film. Results showed that bubble size increased with longer vacuum times, interpreted as coalescence due to limited degassing and silicone viscosity. The density of 3D boluses ranged from 0.980 to 1.104 g/cm³ (commercial: 0.988 g/cm³), with HU values of +240 to +250 (commercial: −110). In point-wise comparisons, mean dose differences were less than 1% for 1- and 5 min samples and approximately 1% for 10 min, with all conditions within |Δ| ≤ 3%. Film analysis confirmed equivalent surface dose distributions. These findings demonstrate, for the first time, that microscopic bubbles in 3D-printed silicone boluses have negligible clinical impact, supporting their safe adoption without requiring complex degassing procedures. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

Background: Sustained-release (SR) formulations of non-steroidal anti-inflammatory drugs (NSAIDs) aim to prolong therapeutic activity, reduce dosing frequency, and improve patient adherence. However, currently marketed SR NSAIDs exhibit persistent limitations, including incomplete control over release kinetics, high interpatient variability in bioavailability, limited reduction in gastrointestinal adverse effects, and insufficient dose flexibility for individualized therapy. In many cases, conventional excipients and release mechanisms remain predominant, leaving drug-specific physicochemical and pharmacokinetic constraints only partially addressed. These gaps highlight the need for a comprehensive synthesis of recent technological advances to guide the development of more effective, patient-centered delivery systems. Methods: A narrative literature review was conducted using Web of Science and PubMed databases to identify original research articles and comprehensive technological studies on oral SR formulations of NSAIDs and paracetamol published between January 2020 and March 2025. Inclusion criteria focused on preclinical and technological research addressing formulation design, excipient innovations, and manufacturing approaches. Results: Sixty-four studies met the inclusion criteria, encompassing polymeric matrices (31%), lipid-based carriers (18%), microspheres/hydrogel beads/interpenetrating polymer networks (30%), nanostructured systems (11%), and hybrid platforms (10%). The most common strategies involved pH-dependent release, mucoadhesive systems, and floating drug delivery, aiming to optimize release kinetics, minimize mucosal irritation, and sustain therapeutic plasma levels. Advances in manufacturing—such as hot-melt extrusion, 3D printing, electrospinning, and spray drying—enabled enhanced control of drug release profiles, improved stability, and in some cases up to 30–50% prolongation of release time or reduction in Cmax fluctuations compared with conventional formulations. Conclusions: Recent formulation strategies show substantial potential to overcome long-standing limitations of SR NSAID delivery, with expected benefits for patient compliance and quality of life through reduced dosing frequency, better tolerability, and more predictable therapeutic effects. Nevertheless, integration of in vitro performance with pharmacokinetic and clinical safety outcomes remains limited, and the translation to clinical practice is still in its early stages. This review provides a comprehensive overview of current technological trends, identifies persisting gaps, and proposes future research directions to advance SR NSAID systems toward safer, more effective, and patient-focused therapy. Full article

Polylactic acid (PLA) is an eco-friendly polymer known for its biodegradability and biocompatibility, yet its properties are sensitive to recycling and sterilization. These processes may cause chain scission and structural irregularities, leading to reduced strength, brittleness, or unpredictable deformation. In this study, PLA and recycled PLA (Re-PLA) specimens were produced by FDM 3D printing with different infill rates (25%, 50%, 75%), layer thicknesses (0.15, 0.20, 0.25 mm), and printing orientations (0°, 45°, 90°). Steam sterilization at 121 °C and 1 bar for 15 min simulated biomedical conditions. Mechanical, surface, degradation, and biocompatibility properties were examined using three-point bending, roughness measurements, SEM, and cell viability tests. Results showed that infill rate was the main parameter affecting flexural strength and surface quality, while orientation increased roughness. Sterilization and recycling made deformation less predictable, particularly in St-Re-PLA. SEM revealed stronger bonding at higher infill, but more brittle fractures in PLA and Re-PLA, while sterilized specimens showed ductile features. No visible degradation occurred at any infill level. Regression analysis confirmed that second-order polynomial models effectively predicted flexural strength, with layer thickness being most influential. These findings provide critical insights into optimizing PLA and Re-PLA processing for biomedical applications, particularly in the production of sterilizable and recyclable implantable devices. Full article

Optimizing printing parameters is crucial for enhancing the efficiency, surface quality, and dimensional accuracy of Fused Deposition Modeling (FDM) processes. A review of numerous publications reveals that most scholars analyze factors such as nozzle diameter and printing speed, while few investigate the impact of layer thickness, infill density, and shell layer count on print quality. Therefore, this study employed 3D slicing software to process the three-dimensional model and design printing process parameters. It systematically investigated the effects of layer thickness, infill density, and number of shells on printing time and geometric accuracy, quantifying the evaluation through volumetric error. Using an ABS connecting rod model, optimal parameters were determined within the defined range through orthogonal experimental design and signal-to-noise ratio (S/N) analysis. Subsequently, a backpropagation (BP) neural network was constructed to establish a predictive model for process optimization. Results indicate that parameter selection significantly impacts print duration and surface quality. Validation confirmed that the combination of 0.1 mm layer thickness, 40% infill density, and 5-layer shell configuration achieves the highest dimensional accuracy (minimum volumetric error and S/N value). Under this configuration, the volumetric error rate was 3.062%, with an S/N value of −9.719. Compared to other parameter combinations, this setup significantly reduced volumetric error, enhanced surface texture, and improved overall print precision. Statistical analysis indicates that the BP neural network model achieves a Mean Absolute Percentage Error (MAPE) of no more than 5.41% for volume error rate prediction and a MAPE of 5.58% for signal-to-noise ratio prediction. This validates the model’s high-precision predictive capability, with the established prediction model providing effective data support for FDM parameter optimization. Full article

The growing interest in smart buildings and the integration of IoT-based technologies is driving the development of new tools for monitoring and optimizing indoor environmental quality (IEQ). However, many existing solutions remain expensive, invasive and inflexible. This paper presents the design and validation of a compact, low-cost, and real-time sensor system, conceived for seamless integration into indoor environments. The system measures key parameters—including air temperature, relative humidity, illuminance, air quality, and sound pressure level—and is embeddable in standard office equipment with minimal impact. Leveraging 3D printing and open-source hardware/software, the proposed solution offers high affordability (approx. EUR 33), scalability, and potential for workspace retrofits. To assess the system’s performance and relevance, dynamic simulations were conducted to evaluate metrics such as the Mean Radiant Temperature (MRT) and illuminance in an open office layout. In addition, field tests with a functional prototype enabled model validation through on-site measured data. The results highlighted significant local discrepancies—up to 6.9 °C in MRT and 28 klx in illuminance—compared to average conditions, with direct implications for thermal and visual comfort. These findings demonstrate the system’s capacity to support high-resolution environmental monitoring within IoT-enabled buildings, offering a practical path toward the data-driven optimization of occupant comfort and energy efficiency. Full article

Hernia repair is among the most frequent procedures in general surgery, traditionally performed with synthetic meshes such as polypropylene. While effective in reducing recurrence, these materials are biologically inert and often trigger chronic inflammation, fibrosis, pain, and impaired abdominal wall function, with a significant impact on long-term quality of life. A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus databases, and relevant preclinical, clinical, and review articles were synthesized within a narrative review framework. Recent advances in tissue engineering propose a shift from passive reinforcement to regenerative strategies based on biomimetic scaffolds, nanomaterials, and nanocomposites that replicate the extracellular matrix, enhance cell integration, and provide controlled drug delivery. Nanotechnology enables localized release of anti-inflammatory, antimicrobial, and pro-angiogenic agents, while electrospun nanofibers and composite scaffolds improve strength and elasticity. In parallel, 3D printing allows for patient-specific implants with tailored architecture and regenerative potential. Although preclinical studies show encouraging results, clinical translation remains limited by cost, regulatory constraints, and long-term safety uncertainties. Overall, these innovations highlight a transition toward personalized and regenerative hernia repair, aiming to improve durability, function, and patient quality of life. Full article

Background/Objectives: This systematic review and meta-analysis aimed to compare the mechanical and surface properties of three-dimensional (3D) printed and conventionally polymerized acrylic resins. Methods: A comprehensive search of four electronic databases (PubMed, Embase, Scopus, and Web of Science) was conducted to identify in vitro studies evaluating impact strength, elastic modulus, surface hardness, and surface roughness. Study quality was assessed using design-specific evaluation tools. When sufficient homogeneous data were available, a meta-analysis was performed. Results: The initial search yielded 942 potentially relevant records. Fifteen studies met the criteria for qualitative synthesis, and 13 were included in the meta-analysis. All studies were in vitro and were rated as having moderate to high methodological quality. Conclusions: Although conventional acrylic resins currently demonstrate superior mechanical strength, 3D-printed materials exhibit comparable surface properties and continue to evolve rapidly. Additive manufacturing technologies show promise as a viable and effective alternative for future prosthodontic applications. Full article

Copper is a critical material for energy transition and green technologies, making its sustainable use increasingly important. Its superior thermal and electrical conductivity make it highly well-suited for additive manufacturing (AM). In this study, copper sourced from offshore electrical cables was upcycled to produce high-quality metal powder for AM. The scrap was processed to separate the metal from plastic and rubber, then refined through ultrasonic atomization, achieving a purity of ~99.5% wt.% with minimal impurities. Characterization demonstrated good flowability, apparent and tap densities, and a well-distributed particle size. To assess its performance in AM, the powder was printed using Directed Energy Deposition (DED) with a laser beam, confirming its high printability and compatibility with the base material. Finally, a comparative Life Cycle Assessment (LCA) revealed a significant environmental advantage of the recycling-based process over conventional mining, reducing global warming potential by more than 70%. These findings highlight the importance of feedstock origin in AM sustainability and support the adoption of circular economy strategies to lower the environmental footprint of advanced manufacturing. Full article