Search Results (47,422)

This study compared widely used nonabsorbable and monofilament suture materials tied with three different configurations and two different suture techniques. Three suture materials (polytetrafluoroethylene, polypropylene, and nylon) were tied with either Laurell–Gottlow or the horizontal mattress suturing techniques using three different knot configurations: A (2 = 1 = 1 = 1 = 1), B (2 × 1 = 1 = 1 = 1), and C (1 × 2 = 1 = 1 = 1) on an experimental platform manufactured using a three-dimensional printer. Specimens underwent microtensile testing to determine maximum load failure and elongation rates at baseline and after 7 days of artificial saliva immersion. The Laurell–Gottlow yielded significantly lower elongation rates and higher failure load than the horizontal mattress suturing technique using nylon and polypropylene sutures at both time points (p < 0.001). Nylon had a significantly higher failure load and elongation than polypropylene and polytetrafluoroethylene at baseline for both suturing techniques and all three knot configurations (p < 0.001). Configuration C had low failure load values following immersion for all suture materials when using horizontal mattress suturing. Configuration A demonstrated superior failure load following the immersion period for all materials using both techniques. The polytetrafluoroethylene suture remained more stable over time. These findings indicate that the Laurell–Gottlow suturing technique with Configuration A provides better mechanical resistance to external forces when using nonabsorbable monofilament suture materials. Full article

This paper presents the design, fabrication, and experimental validation of a metal 3D-printed mechanically tunable cavity resonator operating in the hybrid TM–coaxial resonant mode for the broadband characterization of liquid permittivity. The proposed structure was developed based on a cylindrical cavity by incorporating a disc-terminated metallic tuning stub, which enables continuous frequency adjustment from 0.5 GHz to 3.0 GHz while maintaining a maximum unloaded Q-factor of 284 at 1 GHz under air-filled conditions. The tuning mechanism allows for precise frequency selection for characterizing materials exhibiting frequency-dependent permittivity. To demonstrate its sensing capability, the resonator was applied to characterize ethanol–water mixtures, where resonant frequency shifts were correlated with ethanol concentration at representative baseline frequencies of 1.00 GHz, 2.00 GHz, and 2.94 GHz. The sensor achieved frequency/dielectric constant resolutions of 0.39, 1.34, and 4.20 MHz and average concentration errors of 1.25%, 3.73%, and 2.49%, respectively. Moreover, polynomial fitting models enabled the accurate extraction of dielectric constants with an average deviation below 0.5% compared with a commercial dielectric probe system. The combination of frequency tunability, compact geometry, and compatibility with additive manufacturing establishes the proposed cavity resonator as a versatile platform for broadband dielectric spectroscopy, chemical sensing, and liquid characterization. Full article

Over the last years collaborative robots have gained great success in manufacturing applications where human and robot work together in close proximity. However, current ISO/TS-15066-compliant implementations often limit the efficiency of collaborative tasks due to conservative speed restrictions. For this reason, this paper introduces a deep-learning-based human–robot–safety framework (HRSF) that aims at a dynamical adaptation of robot velocities depending on the separation distance between human and robot while respecting maximum biomechanical force and pressure limits. The applicability of the framework was investigated for four different deep learning approaches that can be used for human body extraction: human body recognition, human body segmentation, human pose estimation, and human body part segmentation. Unlike conventional industrial safety systems, the proposed HRSF differentiates individual human body parts from other objects, enabling optimized robot process execution. Experiments demonstrated a quantitative reduction in cycle time of up to 15% compared to conventional safety technology. Full article

Introduction: Levodopa (LD) is the most efficacious antiparkinsonian drug. However, long-term conventional LD treatment of Parkinson’s disease (PD) is frequently associated with motor complications. This can be attributed to pulsatile dopaminergic stimulation given the short LD half-life of conventional dosage forms. Tablets capable of delivering more stable and sustained dopaminergic stimulation would better mimic the brain’s natural dopamine activity. Methods: In this study, 3D screen printing technology was used to manufacture oral dosage forms characterized by the sequential release of Carbidopa and Levodopa. This was achieved by separating the two compounds into different compartments within the same dosage form, which were arranged (LXM.5-1) or formulated (LXM.5-2) in a specific way. Both novel dosage forms were compared to conventional immediate release forms such as Sinemet^®. The physicochemical properties of the resulting tablets, LXM.5-1 and LXM.5-2, were assessed in accordance with the USP. Their pharmacokinetic profiles were defined in pigs. Results: The physicochemical properties of LXM.5-1 and LXM.5-2 complied with regulatory requirements. Dissolution studies revealed sequential CD and LD release for both novel dosage forms. They differed regarding the interval between CD and LD release which was shorter for LXM.5-1. PK studies demonstrated that both novel dosage forms exhibited higher LD bioavailability in comparison to Sinemet^®, which was 211.36% and 383.64% for LXM.5-1 and LXM.5-2, respectively. Furthermore, blood levels were more stable and sustained, particularly for LXM.5-2. Conclusions: We conclude that 3D screen-printed LXM.5-1 and LXM.5-2 and variations thereof have the potential to transform the pharmacotherapy of Parkinson’s disease. Full article

Current harmonics in six-phase permanent magnet synchronous motors (PMSMs) arise from inherent asymmetries caused by manufacturing tolerances and nonlinear characteristics in the inverter output. Additionally, magnetic saturation and slight imbalances in the windings introduce flux linkage asymmetries, resulting in both fundamental current imbalance and low-order harmonics. Although these imbalances are minor and do not indicate fault conditions, they can cause uneven copper loss and eventually reduce the overall service life of the motor. This paper proposes a harmonic suppression strategy for mitigating imbalance current harmonics in non-ideal six-phase PMSMs. The method integrates back-electromotive force harmonic feedforward compensation (BEMF-HFC) with harmonic synchronous reference frame current control (HSRF-CC). An imbalance flux linkage harmonic model is developed in simulations to replicate the measured imbalance phase currents and to validate the effectiveness of the proposed strategy. The experimental setup is built using a microcontroller from Texas Instruments (TI), which generates six-phase complementary PWM signals for the power stage and receives feedback signals including phase currents, DC bus voltage, and rotor position. Rotor position is acquired through a 12-pole resolver and a 12-bit resolver-to-digital converter (RDC). The six-phase PMSM used in the tests is specified with 12 poles, a rated DC bus voltage of 600 V, a rated current of 200 Arms, and a rated rotor speed of 1200 rpm. Compared with conventional harmonic suppression strategies that do not target imbalance current harmonics, the proposed method achieves a better current balance and lower total harmonic distortion (THD). At 1200 rpm, the magnitude deviation of the fundamental, third, and fifth current harmonics is reduced from 8.61%, 2.88%, and 2.94% to 1.19%, 1.02%, and 0.5%, respectively. Full article

This study investigates the corrosion and scaling behaviour of Extreme High-speed Laser Application (EHLA)-fabricated corrosion-resistant alloy (CRA) claddings under simulated geothermal brine conditions. EHLA 316L stainless steel and alloy 625 coatings were produced and tested in simulated brine (chloride–carbonate–silica geothermal brine) at 70 °C for 720 h to evaluate the influence of additive manufacturing (AM) microstructures on corrosion performance. The EHLA coatings exhibited dense, metallurgically bonded microstructures with minimal porosity. Microstructural analysis revealed Nb- and Mo-rich segregation in EHLA 625 and fine columnar dendritic morphology in all coatings. EHLA 625 developed a stable passive film with only a thin deposit of Mg-O-containing compounds, whereas EHLA 316L exhibited localised pitting and significant Si- and Mg-containing scale accumulation, especially in as-built conditions. Surface finishing reduced corrosion activity by minimising roughness and defect-driven localised attack. Critical pitting temperature (CPT) tests confirmed the superior localised corrosion resistance of EHLA 625 relative to EHLA 316L under laboratory conditions. While these results indicate promising corrosion and scaling resistance of EHLA coatings, further process optimisation and post-deposition thermal treatments might be required to achieve coating performance comparable to wrought alloys. The results indicate the potential of EHLA-fabricated coatings for producing corrosion and scaling resistance surfaces. Full article

Reliable detection of X-ray tire defects is essential for safety and quality assurance in manufacturing. However, low contrast and high noise make traditional methods unreliable. This paper presents DyReCS-YOLO, a dynamic re-parameterized channel-shuffle network based on YOLOv8. The model introduces a C2f_DyRepFusion module combining dynamic convolution and a shuffle-and-routing mechanism, enabling adaptive kernel adjustment and efficient cross-channel interaction. Experiments on an industrial X-ray tire dataset containing 8326 images across 58 defect categories demonstrate that DyReCS-YOLO achieves an mAP@0.5 of 0.741 and mAP@0.5:0.95 of 0.505, representing improvements of 4.5 and 2.8 percentage points over YOLOv8-s, and 9.2 and 7.7 percentage points over YOLOv11-s, respectively. The precision increases from 0.698 (YOLOv8-s) and 0.668 (YOLOv11-s) to 0.739, while maintaining real-time inference at 189.5 FPS, meeting industrial online detection requirements. Ablation results confirm that the combination of dynamic convolution and channel shuffle improves small-defect perception and robustness. Moreover, DyReCS-YOLO achieves an mAP@0.5 of 0.975 on the public MT defect dataset, verifying its strong cross-domain generalization. Full article

20CrMnTi carburized steel is widely used in the manufacturing of gear transmission components. Gears often operate under complex working conditions that involve high pressure and temperature, and intermittent alternating loads. The material is frequently subjected to repeated compression and resilience cycles, which significantly affect dimensional accuracy and stability. This study investigates the influence of different temperatures and pressures on the compression resilience properties and microhardness of 20CrMnTi steel. The results indicate that temperature and pressure lead to a nonlinear increase in the compression curve, reduce resilience properties and the microhardness of the carburized layer, and affect the microhardness of the matrix. Microstructural analysis indicated that plastic deformation, carbide coarsening, and a lower elastic modulus led to the enlarged compression and diminished microhardness in the carburized layer. Work hardening, dynamic recrystallization, and plastic deformation in the matrix material weaken resilience properties and influence the distribution of microhardness in the matrix. Full article

This study presents the development and evaluation of a technology for producing geopolymer-based granulates, which act as sustainable substitutes for natural aggregates by utilizing waste materials. The technology is demonstrated to be energy-efficient compared to other manufactured aggregate processes (such as sintering), as it relies on a cold-bonding process and achieves self-hardening at room temperature. The granulation of geopolymer materials using an intensive counter-current mixer represents an innovative solution in the field of producing substitutes for natural aggregates. Coal fly ash (CFA) was used as the primary aluminosilicate precursor, with composite regrind from decommissioned wind turbine blades (CR) and steelmaking dust (SD) tested as additives. Reactive solids and alkaline activator liquids were mixed and granulated in a single operation using an intensive counter-current mixer; moistening and surface powdering were applied to improve granule sphericity. The granules were cold-cured at room temperature and characterized after 28 days by grain size distribution, crushing resistance, water absorption, abrasion (micro-Deval), SEM/EDS and leaching tests. The results indicate that the additives significantly improved the mechanical performance: PM + PK granules reached crushing strengths > 6 MPa, while CFA + SD granules reached > 11 MPa, exceeding many commercial lightweight aggregates (such as LECA or Lytag), as detailed in the paper. The CFA + CR granulates exhibited a compact microstructure and the effective immobilization of several heavy metals, whereas the CFA + DS samples demonstrated the excessive leaching of Cr, Pb and Mo. The process achieved a high solid-to-liquid ratio (>2.0), reducing activator consumption. Composite regrind is recommended as a promising additive. Full article

The surface quality of hot-rolled steel products derived from recycled materials is critically impacted by oxide scale formation and adhesion, a behavior significantly influenced by residual silicon (Si) and the processing atmosphere. This study addresses a key research gap by thoroughly investigating the combined effect of water vapor content (10% to 30%) and residual Si content (across various slab types) on scale formation and adhesion, with a direct focus on process optimization to minimize surface defects. Crucially, this research introduces a novel quantitative assessment utilizing a macro-tensile test. This innovative method provides accurate mechanical scale adhesion energy data (measured in J/m²) directly applicable to hot-rolled recycled steel, a technique previously underexplored for this challenging material system. Results reveal that increasing water vapor concentrations significantly accelerate the formation of thicker and more defective oxide scales, thereby directly diminishing scale adhesion strength substantially across tested conditions. Conversely, steel with higher residual Si consistently maintained significantly higher scale adhesion energy than low-Si steel under similar steam conditions. Based on these quantitative findings, this study proposes a specific two-factor strategy for industrial application, strictly minimizing residual Si content while maintaining the furnace water vapor concentration at an intermediate level (approximately 20%). This strategy is shown to optimize scale formation conditions, facilitating efficient scale removal. Such results are crucial for optimizing hot-rolling parameters in recycled steel production, enabling enhanced surface quality and promoting sustainable manufacturing practices by providing a reliable quantitative metric (adhesion energy) for industrial quality control. Full article

Polymethyl methacrylate (PMMA) is widely used in microfluidic device fabrication due to its chemical resistance, low cost, optical transparency, and manufacturing compatibility. However, limited research exists on wall deformations and the minimum achievable wall thickness between machined channels in PMMA via micro-milling. As microfluidic devices require tightly spaced features, identifying the minimum machinable wall thickness is essential for miniaturization and multifunctional integration, enabling rapid and reproducible biomedical testing. This study presents experimental data and finite element modeling on wall deformation characteristics—wall deviation angle, average wall thickness, and minimum machinable wall thickness—between micro-milled PMMA channels. Micro end-milling was performed with varying feed rates, wall thicknesses (50 $\mathsf{\mu }$m, 100 $\mathsf{\mu }$m, 150 $\mathsf{\mu }$m), and milling strategies (direct, radial, axial depth). ANOVA was used to assess parameter influence, and finite element modeling simulated wall bending under the radial depth strategy. Results show that wall thickness, feed rate, and milling strategy significantly affect wall deviation and thickness. Experimental and simulation data revealed consistent trends: 50 $\mathsf{\mu }$m walls showed cracking, base fractures, and geometric deviations, while 100 $\mathsf{\mu }$m and 150 $\mathsf{\mu }$m walls retained structural integrity. A minimum wall thickness of 150 $\mathsf{\mu }$m is necessary to ensure reliable sealing in microfluidic devices. Full article

This paper presents a concept for a Learning Factory (LF) designed for interdisciplinary engineering education. Learning factories are experiential learning environments that bridge the gap between theory and practice while supporting the demands of digital transformation. The proposed LF concept was developed using an integrated approach that assessed stakeholder needs and reviewed institutional infrastructure and capacity. These inputs were triangulated into a concept consisting of five core thematic components: Lean processes as an educational anchor, Enterprise Resource Planning (ERP) systems, Internet of Things (IoT)-based integration, simulation, and physical prototyping. Validation workshops with Small- and Medium-sized Enterprise (SME) managers, academic experts, and students confirmed the perceived relevance of this structure and its potential. The resulting concept focuses on practice-orientated, team-based learning methods that are in line with the principles of Education 4.0. The design sets goals in four key dimensions: educational integration, technological readiness, industrial relevance with SME orientation and flexibility and scalability. These design principles and practical insights can be utilized for future academic implementations of learning factories. Full article

Additive manufacturing (AM) of polyetheretherketone (PEEK) offers a promising route for producing lightweight, biocompatible, and patient-specific medical implants with complex geometries. This study investigates and optimizes fused deposition modeling (FDM) parameters for fabricating small-scale PEEK medical components with improved dimensional accuracy and surface quality. PEEK’s high processing temperature and thermal contraction make precision printing of fine features challenging. A Taguchi design of experiments (L9 orthogonal array) was employed to assess the effects of nozzle temperature, layer height, printing speed, and extrusion width on dimensional deviation and surface roughness using 5 × 5 × 5 mm cube specimens. Dimensional accuracy was quantified along the horizontal and vertical axes, and surface roughness was measured using a stylus profilometer. Statistical analysis showed layer height was the most significant factor affecting horizontal accuracy (p = 0.0225), while printing speed most strongly influenced vertical deviation. The optimal parameters, 450 °C nozzle temperature, 0.06 mm layer height, 7.5 mm/s printing speed, and 0.4 mm extrusion width, achieved mean deviations of 0.013 mm (horizontal) and 0.049 mm (vertical) with a surface roughness of 4.01 µm. Validation using a benchmark model and micro-computed tomography confirmed improved reproduction of small features under these conditions. The results demonstrate that precise control of FDM parameters enables accurate fabrication of sub-millimeter PEEK structures suitable for medical device applications. Full article

Developing bio-based polyurethane (BPU) composites that incorporate bio-oil and wood dust as sources of hydroxyl groups (-OH) presents a compelling approach to advancing sustainable polymer systems. This study examines the impact of isocyanate-to-hydroxyl equivalent ratios and varying proportions of bio-oil and wood dust on the processability and mechanical properties of molded composite panels. Formulations were systematically optimized based on equivalent ratio calculations to enhance extrusion behavior and final structural performance. Extrusion trials demonstrated that an -NCO/-OH ratio of 1.5:1, with 50% wood dust serving as an -OH donor, resulted in the most stable material flow, characterized by minimized surface defects and an ideal viscosity for processing. Compression molding and mechanical testing revealed that a balanced formulation with 50% bio-oil and 50% wood dust, with an equivalent ratio of -OH groups, achieved the best combination of Young’s modulus, stress, and strain performance, even under wet conditions. SEM confirmed improved filler dispersion and interfacial adhesion in these optimized systems. Although full 3D-printing trials were not conducted, the observed extrusion stability and controlled curing behavior indicate strong potential for application in extrusion-based additive manufacturing. These results highlight that precise resin–filler balancing enables continuous extrusion, structural resilience, and reduced activation energy, reinforcing the viability of BPUs as scalable, sustainable materials for construction and additive manufacturing. Full article