Search Results (583)

This work presents the design, fabrication, and characterization of a three-dimensional FeCo-based flux-modulator microwinding intended for integration into high-torque axial-flux Vernier micromotors. The proposed micromotor architecture modulates the stator magnetic flux using 12 magnetically isolated FeCo teeth interacting with an 11-pole permanent-magnet rotor. The design requires the manufacturing of complex three-dimensional micrometric parts, including three teeth and a cylindrical core. Such a complex design cannot be manufactured using conventional micromanufacturing lithography or 2D planar methods. The flux-modulator envelope dimensions are 250 μm outer diameter and 355 μm height. It is manufactured using a femtosecond laser-machining process that preserves factory-finished surfaces and minimizes heat-affected zones. In addition, this micrometric part has been wound using 20 μm diameter enamelled copper wire. A dedicated magnetic clamping fixture is developed to enable multilayer microwinding of the integrated core, producing a 17-turn inductor with a 60.6% fill factor—the highest reported for a manually wound ferromagnetic-core microcoil of this scale. Geometric and magnetic characterization validates the simulation model and demonstrates the field distribution inside the isolated core. The results establish a viable micromanufacturing workflow for complex 3D FeCo microwindings, supporting the development of next-generation high-performance MEMS micromotors. Full article

This study systematically investigates the effects of key process parameters in wire arc additive manufacturing (WAAM) on the surface morphology, geometric dimensions, microstructure, and microhardness of single-bead single-layer deposits fabricated from 2209 duplex stainless steel. Using a controlled variable approach, the influences of wire feed speed, travel speed, oscillation pattern, oscillation frequency, and oscillation amplitude on the deposition quality were examined. Experimental results indicate that wire feed speed and travel speed significantly affect the bead width, height, and fusion zone morphology, with optimal ranges identified as 4.5–6.5 m/min and 5–6 mm/s, respectively. Among the oscillation patterns, sinusoidal and figure-eight trajectories resulted in uniform deposition distribution and a refined microstructure, whereas the circular pattern led to fish-scale surface features and coarse grains. The optimal oscillation frequency and amplitude were determined to be 4 Hz and 4 mm, respectively, under which the deposits exhibited high surface quality, no defects other than the depression in the arc extinction zone, and the microhardness remains stable in the range of 280–290 HV. Comprehensive analysis indicates that investigating the influence of these process parameters on the morphology, microstructure, and hardness of DSS2209 single-bead deposits can effectively enhance the overall performance of WAAM-fabricated 2209 duplex stainless steel components, thereby providing a reliable foundation for subsequent multi-layer and multi-bead deposition. Full article

Multimode interference photonic nanodevices have been increasingly used due to their broad functionality. In this study, we present a methodology based on machine learning algorithms for inverse design capable of providing the output port position (x-axis coordinate) and MMI region length (y-axis coordinate) for achieving higher optical signal transfer power. This is sufficient to design Multimode Interference 1 × 2, 1 × 3, and 1 × 4 nanodevices as power splitters in the wavelength range between 1350 and 1600 nm, which corresponds to the E, S, C, and L bands of the optical communications window. Using Multilayer Perceptron artificial neural networks, trained with k-fold cross-validation, we successfully modeled the complex relationship between geometric parameters and optical responses with high precision and low computational cost. The results of this project meet the requirements for photonic device projects of this nature, demonstrating excellent performance and manufacturing tolerance, with insertion losses ranging from 0.34 dB to 0.58 dB. Full article

Circular economy and Industry 4.0 principles are increasingly shaping industrial practices. In the textile sector, environmental impacts and low recyclability make circularity a critical priority. This study focuses on enhancing both circularity and operational efficiency in a Portuguese manufacturer of labels and trimmings. Achieving this requires the collection of relevant data and identification of the factors that most influence operational performance, while linking these to circularity outcomes. To support this effort, the paper presents two complementary methodologies: Multi-layer Stream Mapping (MSM) for evaluating manufacturing efficiency and the Overall Circularity Index (OCI) for assessing circularity performance. MSM provides a detailed analysis of process efficiency, identifying sources of waste and summarizing results through user-friendly scorecards that highlight high-impact improvement areas. The OCI measures a company’s circularity on a scale from 0 to 1—where 1 represents full circularity—using strategic indicators across environmental, material, economic, and social dimensions. The MSM revealed an overall efficiency of 71%, whereas the OCI resulted in a final score of 0.516. When applied together, MSM and the OCI form a straightforward, iterative, and effective framework for diagnosing strengths and weaknesses in the manufacturing process, supporting evidence-based decision-making and guiding the company’s transition toward more circular and efficient operations. Full article

The present work uses numerical methods to explore the impact of spatial orientation on the behavior of molten pool and thermal responses during the laser–Cold Metal Transfer (CMT) hybrid additive manufacturing of metallic cladding layers. Based on the traditional double-ellipsoidal heat source model, an adaptive CMT arc heat source model was developed and optimized using experimentally calibrated parameters to accurately represent the coupled energy distribution of the laser and CMT arc. The improved model was employed to simulate temperature and velocity fields under horizontal, transverse, vertical-up, and vertical-down orientations. The results revealed that variations in gravity direction had a limited effect on the overall molten pool morphology due to the dominant role of vapor recoil pressure, while significantly influencing the local convection patterns and temperature gradients. The simulations further demonstrated the formation of keyholes, dual-vortex flow structures, and Marangoni-driven circulation within the molten pool, as well as the redistribution of molten metal under different orientations. In multi-layer deposition simulations, optimized heat input effectively mitigated excessive thermal stresses, ensured uniform interlayer bonding, and maintained high forming accuracy. This work establishes a comprehensive numerical framework for analyzing orientation-dependent heat and mass transfer mechanisms and provides a solid foundation for the adaptive control and optimization of laser–CMT hybrid additive manufacturing processes. Full article

Biomedical antennas are essential components in modern healthcare systems, supporting wireless communication, physiological monitoring, diagnostic imaging, and therapeutic energy delivery. Their performance is strongly influenced by proximity to the human body, creating challenges such as impedance detuning, signal absorption, and size constraints that motivate new materials and fabrication approaches. This work reviews recent advances enabling next-generation wearable and implantable antennas, with emphasis on printed electronics, additive manufacturing, flexible hybrid integration, and metamaterial design. Methods discussed include 3D printing and inkjet, aerosol jet, and screen printing for fabricating conductive traces on textiles, elastomers, and biodegradable substrates, as well as multilayer Flexible Hybrid Electronics that co-integrate sensing, power management, and RF components into thin, body-conforming assemblies. Key results highlight how metamaterial and metasurface concepts provide artificial control over dispersion, radiation, and near-field interactions, enabling antenna miniaturization, enhanced gain and focusing, and improved isolation from lossy biological tissue. These approaches reduce SAR, stabilize impedance under deformation, and support more efficient communication and energy transfer. The review concludes that the convergence of novel materials, engineered electromagnetic structures, and AI-assisted optimization is enabling biomedical antennas that are compact, stretchable, personalized, and highly adaptive, supporting future developments in unobtrusive monitoring, wireless implants, point-of-care diagnostics, and continuous clinical interfacing. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

The motivation for this work is to study the cause and present mitigation for some challenges faced in preparing porous silicon. This enables benefiting from the appealing benefits of porous silicon that offers a wide range, simple technique for varying the refractive index. Such challenges include the refractive index values, sensitivity to oxidation, some fabrication parameters, and other factors. Additionally, highly doped p-type silicon is preferred to form porous silicon, but it causes high losses, which necessitates its detachment. We investigate some possible causes of refractive index change, especially after detaching the fabricated layers from the silicon substrate. Thereby, we could recommend simple but essential precautions during fabrication to avoid such a change. For example, the native oxide formed in the pores has a role in changing the porosity upon following some fabrication sequence. Oppositely, intrinsic stress doesn’t have a significant role. On another aspect, the effect of differing etching/break times on the filter’s responses has been studied, along with other subtle details that may affect the lateral and depth homogeneity, and thereby the process success. Solving such homogeneity issues allowed reaching thick layers not suffering from the gradient index. It is worth highlighting that several approaches have been reported; unlike these, our method doesn’t require sophisticated equipment that might not be available in every lab. To well characterize the thin films, it has been found essential that freestanding monolayers are used for this purpose. From which, the wavelength-dependent refractive index and absorption coefficient have been determined in the near infrared region (1000–2500 nm) for different fabricated conditions. Excellent fitting with the measured interference pattern has been achieved, indicating the accurate parameter extraction, even without any ellipsometry measurements. This also demonstrates the refractive index homogeneity of the fabricated layer, even with a large thickness of over 16 µm. Subsequently, multilayer structures have been fabricated and tested, showing the successful nano-manufacturing methodology. Full article

This study investigates the predictive modeling of total Polycyclic Aromatic Hydrocarbon (PAH) concentrations in smoked fish products based on various smoking parameters using machine learning techniques in the Waikato Environment for Knowledge Analysis (WEKA) software environment. Key input variables included fish fat content, smoking temperature, and wood type, all of which were statistically significant predictors of PAH levels (p < 0.05). A multiple linear regression analysis conducted in SPSS revealed a strong correlation between predictors and PAH concentration (r = 0.801), with an explained variance of 64.1% (R² = 0.641) and a standard error of 3.52. Among the evaluated machine learning algorithms—Linear Regression, SMOreg, Multilayer Perceptron, M5P, Random Forest, and IBk—performance was assessed using five criteria: Correlation Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). All models were validated using 10-fold cross-validation. For classification tasks based on fish species, Logistic Regression outperformed the Random Forest and J48 algorithms, indicating superior predictive capability. This integrated analytical framework demonstrates the effectiveness of machine learning in food safety monitoring and provides a scientific basis for optimizing smoking processes to mitigate PAH contamination. Overall, the findings underscore the practical value of machine learning tools in the predictive modeling of PAH contamination in smoked fish. The approach not only offers high predictive accuracy but also serves as a scientific framework for improving food safety by optimizing smoking conditions to minimize PAH formation. This integrated model can aid food technologists and manufacturers in establishing safer processing parameters while maintaining product quality. Full article

Fused Filament Fabrication (FFF) plays a critical role in several application fields due to its affordability and manufacturing versatility. However, FFF reliability remains vulnerable to rapid environmental and operational variations, which directly influence the dimensional precision and mechanical properties of printed parts. To address these challenges, this study presents a simulation-based computational framework for the real-time early-warning supervision of FFF systems. The proposed multilayer architecture integrates high-throughput data acquisition, distributed computing, and dynamic analysis to proactively detect deviations from optimal conditions. Architectural verification follows a simulation-first methodology designed to replicate the operational dynamics of standard FFF hardware. By employing telemetry streams to test the decision-making pipeline, the study isolates computational performance, such as throughput and latency, from the confounding variables of physical hardware. This approach enables a precise, deterministic assessment of the system’s responsiveness, serving as a foundational de-risking step prior to empirical implementation. Numerical results of this study show that the integrated distributed computing model successfully manages high-frequency telemetry with a response time within the operational safety margins, confirming the architectural viability of the proposed solution. By providing insights into system behavior prior to physical deployment, this simulation-first strategy mitigates implementation risks and offers practical guidance for developing autonomous additive manufacturing workflows, advancing the transition toward intelligent industrial FFF. Full article

This review examines modern approaches to layer formation in solid oxide fuel cells (SOFCs), focusing on traditional, thin-film, and additive manufacturing methods. A systematic comparison of technologies, including slip casting, screen printing, CVD, PLD, ALD, HiPIMS, inkjet, aerosol, and microextrusion printing, is provided. It is shown that traditional methods remain technologically robust but are limited in their capabilities for miniaturization and interfacial architecture design. Modern thin-film and additive approaches provide high spatial accuracy, improved ion-electron characteristics, and flexibility in the design of multilayer structures; however, they require addressing issues related to scalability, ink stability, interfacial compatibility, and reproducibility. Particular attention is paid to interfacial engineering methods, such as functionally graded layers, nanostructured infiltration, and temperature-controlled 3D printing. Key challenges are discussed, including thermal instability of materials, the limited gas impermeability of ultra-thin electrolytes, and degradation during long-term operation. Development prospects lie in the integration of hybrid methods, the digitalization of deposition processes, and the implementation of intelligent control of printing parameters. The presented analysis forms the basis for further research into the scalable and highly efficient production of next-generation SOFCs designed for low-temperature operation and long-term operation in future energy systems. Full article

In this study, regression-based machine learning models were developed to predict the melt pool width and depth formed during the Laser Powder Bed Fusion (LPBF) process for Fe-3.4Si and Fe-6Si alloys. Based on experimentally obtained melt pool width and depth data, a total of 11 regression models were trained and evaluated, and hyperparameters were optimized via Bayesian optimization. Key process parameters were identified through data preprocessing and feature engineering, and SHAP analysis confirmed that the input energy had the strongest influence on both melt pool width and depth. The comparison of prediction performance revealed that the support vector regressor with a linear kernel (SVR_lin) exhibited the best performance for predicting melt pool width, while the multilayer perceptron (MLP) model achieved the best results for predicting melt pool depth. Based on these trained models, a power–velocity (P-V) process map was constructed, incorporating boundary conditions such as the overlap ratio and the melt pool morphology. The optimal input energy range was derived as 0.45 to 0.60 J/mm, ensuring stable melt pool formation. Specimens manufactured under the derived conditions were analyzed using 3D X-ray CT, revealing porosity levels ranging from 0.29% to 2.89%. In particular, the lowest porosity was observed under conduction mode conditions when the melt pool depth was approximately 1.0 to 1.5 times the layer thickness. Conversely, porosity tended to increase in the transition mode and lack of fusion regions, consistent with the model predictions. Therefore, this study demonstrated that a machine learning-based regression model can reliably predict melt pool characteristics in the LPBF process of Fe-Si alloys, contributing to the development of process maps and optimization strategies. Full article

This study investigates the interlayer properties and sustainability of 3D-printed ultra-high-performance concrete (UHPC) modified with antimony tailings (ATs). The different AT ratios considered were 2.7, 5.4, 8.1, 10.8, and 13.5 wt% additions. The mechanical experiments show the optimal concentration resulting in compressive and flexural strength of 11.2% and 17.2% enhancement at 28 days, respectively. SEM analysis revealed that AT enhances the interlayer strength of 3D-printed UHPC and influences the anisotropic behavior of the matrix around steel fibers. X-CT demonstrated that increasing the AT from the compared group to 13.5% reduced the pore volume from 2.02% to 0.30%. Furthermore, an environmental impact assessment of the 10.8 wt% AT exhibited a 32.5% reduction in key indicators including abiotic depletion (ADP), acidification potential (AP), global warming potential (GWP), and ozone depletion potential (ODP). Consequently, UHPC incorporating AT offers superior environmental sustainability in the practical construction of 3D-printed concrete. This research provides practical guidance in optimizing 3D-printed UHPC engineering, further facilitating the integrated design and manufacturing of multi-layer structures. Full article

Augmented reality (AR) near-eye displays (NEDs) couple microdisplay image light to the human eye via integrated optical modules, enabling seamless virtual–real fusion. As core components that synergistically transmit and diffract light, diffractive waveguides are promising for next-generation AR NEDs but face two bottlenecks: compromised full-color performance in single-layer structures caused by grating dispersion and lack of scalable fabrication technologies. To address these, we first propose a mass-production-compatible workflow based on deep ultraviolet (DUV) lithography for large-area nanostructured optics. This workflow enables high-precision wafer-level production with 200 mm wafers and nine dies per wafer, overcomes scalability issues, and is fully compatible with straight-configuration nanostructures to ensure manufacturing feasibility. Leveraging this workflow, we develop a single-layer diffractive waveguide system for AR NEDs, which comprises a thin glass substrate, a broadband high-efficiency multi-layer dielectric in-coupler, and a 2D out-coupler that concurrently expands and out-couples light. Rigorous coupled wave analysis (RCWA) optimized coupler diffraction, while ray tracing refined guided light intensity and significantly improved exit pupil uniformity. This work establishes a foundation for full-color, high-efficiency AR waveguides and provides a scalable paradigm for large-area nanostructured optical systems such as telescopes and lithography equipment. Full article