Search Results (132)

Polymer-based photonic sensors are emerging as cost-effective, scalable alternatives to conventional silicon and glass photonic platforms, offering unique advantages in flexibility, functionality, and manufacturability. This review provides a comprehensive assessment of recent advances in polymer photonic sensing technologies, focusing on material systems, fabrication techniques, device architectures, and application domains. Key polymer materials, including PMMA, SU-8, polyimides, COC, and PDMS, are evaluated for their optical properties, processability, and suitability for integration into sensing platforms. High-throughput fabrication methods such as nanoimprint lithography, soft lithography, roll-to-roll processing, and additive manufacturing are examined for their role in enabling large-area, low-cost device production. Various photonic structures, including planar waveguides, Bragg gratings, photonic crystal slabs, microresonators, and interferometric configurations, are discussed concerning their sensing mechanisms and performance metrics. Practical applications are highlighted in environmental monitoring, biomedical diagnostics, and structural health monitoring. Challenges such as environmental stability, integration with electronic systems, and reproducibility in mass production are critically analyzed. This review also explores future opportunities in hybrid material systems, printable photonics, and wearable sensor arrays. Collectively, these developments position polymer photonic sensors as promising platforms for widespread deployment in smart, connected sensing environments. Full article

Additive Manufacturing (AM) techniques, such as Fused Deposition Modeling (FDM) and Stereolithography (SLA), are increasingly adopted in various high-demand sectors, including the aerospace, biomedical engineering, and automotive industries, due to their design flexibility and material adaptability. However, the tribological performance and surface integrity of parts manufactured by AM are the biggest functional deployment challenges, especially in wear susceptibility or load-carrying applications. The current review provides a comprehensive overview of the tribological challenges and surface engineering solutions inherent in FDM and SLA processes. The overview begins with a comparative overview of material systems, process mechanics, and failure modes, highlighting prevalent wear mechanisms, such as abrasion, adhesion, fatigue, and delamination. The effect of influential factors (layer thickness, raster direction, infill density, resin curing) on wear behavior and surface integrity is critically evaluated. Novel post-processing techniques, such as vapor smoothing, thermal annealing, laser polishing, and thin-film coating, are discussed for their potential to endow surface durability and reduce friction coefficients. Hybrid manufacturing potential, where subtractive operations (e.g., rolling, peening) are integrated with AM, is highlighted as a path to functionally graded, high-performance surfaces. Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. Through material-level innovations, process optimization, and surface treatment techniques integration, the article provides actionable guidelines for researchers and engineers aiming at performance improvement of FDM and SLA-manufactured parts. Future directions, such as smart tribological, sustainable materials, and AI-based process design, are highlighted to drive the transition of AM from prototyping to end-use applications in high-demand industries. Full article

This study addresses the urgent demand for high-precision manufacturing of curved components by developing a fully servo-driven multi-axis controlled four-roll bending machine. By integrating a modular symmetric roller system design with a distributed hierarchical motion control architecture, we achieved substantial enhancements in scalability, forming stability, and machining accuracy. The mechanical system underwent static simulation optimization using SolidWorks Simulation, ensuring maximum stress in the guiding mechanism was controlled below $7.118\times {10}^3$ N/m². ABAQUS-based roll-bending dynamic simulations validated the geometric adaptability and process feasibility of the proposed mechanical configuration. A master-slave dual-core control architecture was implemented in the control system, enabling synchronized error ≤ 0.05 mm, dynamic response time ≤ 10 ms, and positioning accuracy of ±0.01 mm through collaborative control of the master controller and servo drives. Experimental validation demonstrated that the machine achieves bending errors within 1%, with an average forming error of 0.798% across various radii profiles. The arc integrity significantly outperforms conventional equipment, while residual straight edge length was reduced by 86.67%. By adopting fully servo-electric cylinder actuation and integrating a C#-developed human–machine interface with real-time feedback control, this research effectively enhances roll-bending precision, minimizes residual straight edges, and exhibits broad industrial applicability. Full article

Roll-to-roll (R2R) manufacturing processes demand precise control of web or yarn velocity and tension, alongside robust mechanisms for handling system failures. This paper presents an integrated approach combining high-performance control with reliable fault detection for an experimental R2R system. A model-based cascade control strategy is designed, incorporating system identification, radius compensation for varying roll diameters, and a Kalman filter to mitigate load sensor noise, ensuring accurate regulation of yarn velocity and tension under normal operating conditions. In parallel, a data-driven fault detection layer uses Gaussian Process Regression (GPR) models, trained offline on healthy operating data, to predict yarn tension and motor speeds. During operation, discrepancies between measured and GPR-predicted values that exceed predefined thresholds trigger an immediate shutdown of the system, preventing material loss and equipment damage. Experimental trials demonstrate tension regulation within ±0.02 N and velocity errors below ±5 rad/s across varying roll diameters, while yarn-break and motor-fault scenarios are detected within a single sampling interval (<100 milliseconds) with zero false alarms. This study validates the integrated system’s capability to enhance both the operational precision and resilience of R2R processes against critical failures. Full article

This paper proposes a miniaturized design for a terahertz tri-mirror compact antenna test range (CATR) system, composed of a square-aperture paraboloid primary mirror with a side length of 0.2 m and two shaped mirrors with circular apertures of 0.06 m and 0.07 m in diameter. The design first employs the cross-polarization cancelation method based on beam mode expansion to determine the geometric configuration of the system, thereby enabling the structure to exhibit low cross-polarization characteristics. Subsequently, the shaped mirrors, with beamforming and wave-front control capabilities, are synthesized using dynamic ray tracing based on geometric optics (GO) and the dual-paraboloid expansion method. Finally, the strong edge diffraction effects induced by the small-aperture primary mirror are suppressed by optimizing the desired quiet-zone (QZ) field width, adjusting the feed-edge taper, and incorporating rolled-edge structures on the primary mirror. Numerical simulation results indicate that within the 100–500 GHz frequency band, the system’s cross-polarization level is below −40 dB, while the amplitude and phase ripples of the co-polarization in the QZ are, respectively, less than 1.6 dB and 10°, and the QZ usage ratio exceeds 70%. The designed CATR was manufactured and tested. The results show that at 183 GHz and 275 GHz, the measured co-polarization amplitude and phase ripples in the system’s QZ are within 1.8 dB and 15°, respectively. While these values deviate slightly from simulations, they still meet the CATR evaluation criteria, which specify QZ co-polarization amplitude ripple < 2 dB and phase ripple < 20°. The overall physical structure sizes of the system are 0.61 m × 0.2 m × 0.66 m. The proposed miniaturized terahertz tri-mirror CATR design methodology not only enhances the QZ characteristics but also significantly reduces the spatial footprint of the entire system, demonstrating significant potential for practical engineering applications. Full article

A barrier to the adoption of floating offshore wind turbines is their high cost relative to conventional fixed-bottom wind turbines. The largest contributor to this cost disparity is generally the floating substructure, due to its large size and complexity. Typically, a primary driver of the geometry and size of a floating substructure is the extreme environmental load case of Region 4, where platform loads are the greatest due to the impact of extreme wind and waves. To address this cost issue, a new concept for a floating offshore wind turbine’s substructure, its moorings, and anchors was optimized for a reference 10-MW turbine under extreme load conditions using OpenFAST. The levelized cost of energy was minimized by fixing the above-water turbine design and minimizing the equivalent substructure mass, which is based on the mass of all substructure components (stem, legs, buoyancy cans, mooring, and anchoring system) and associated costs of their materials, manufacturing, and installation. A stepped optimization scheme was used to allow an understanding of their influence on both the system cost and system dynamic responses for the extreme parked load case. The design variables investigated include the length and tautness ratio of the mooring lines, length and draft of the cans, and lengths of the legs and the stem. The dynamic responses investigated include the platform pitch, platform roll, nacelle horizontal acceleration, and can submergence. Some constraints were imposed on the dynamic responses of interest, and the metacentric height of the floating system was used to ensure static stability. The results offer insight into the parametric influence on turbine motion and on the potential savings that can be achieved through optimization of individual substructure components. A 36% reduction in substructure costs was achieved while slightly improving the hydrodynamic stability in pitch and yielding a somewhat large surge motion and slight roll increase. Full article

As an eco-efficient short-process manufacturing technique for aluminum alloys, twin-belt continuous casting and direct rolling (TBCCR) demonstrates significant production advantages. In this study, an Al-Fe-Si alloy system with different Fe-rich intermetallics (α-AlFe(Mn)Si and β-AlFe(Mn)Si) via TBCCR was developed for new energy vehicle batteries, utilizing the Computer Coupling of Phase Diagrams and Thermochemistry (CALPHAD) technique. Comprehensive microstructure and surface segregation analyses of continuous casted ingots and direct-rolled sheets revealed that the Al-Fe-Si alloy with a combined Fe + Si content of 0.7% and an optimal Fe/Si atomic ratio of 3:1 (FS31) presents optimized mechanical properties: ultimate tensile strength of 145.8 MPa, elongation to failure of 5.7%, accompanied by a cupping value of 6.64 mm. Notably, Mn addition further refined the grain structure of casting ingots and enhanced the strength of both ingots and rolled sheets. Among the experimental alloys, FS14 (optimal Fe/Si atomic ratio of 1:4) sheets displayed the least surface segregation upon Mn incorporation. Through systematic optimization, an Al-Fe-Si-Mn alloy composition (Fe + Si = 0.7%, Fe/Si = 1:4 atomic ratio, 0.8 wt.% Mn) was engineered for TBCCR processing, achieving enhanced comprehensive performance: ultimate tensile strength of 189.4 MPa, elongation to failure of 7.32%, and cupping value of 7.71 mm. This composition achieves an optimal balance between grain refinement, mechanical properties (strength–plasticity synergy), formability (cupping value), and corrosion resistance (corrosion current density). The performance optimization strategy integrates synergistic improvements in strength, ductility, and corrosion resistance, providing valuable guidance for developing high-performance aluminum alloys suitable for the TBCCR process. Full article

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect the final quality of the product in the steel rolling process. The system integrates a series of modules, including event filtering, automatic expert knowledge extraction, and decision-making neural networks, developed in a phased approach. The experimental results, using a three-year historical dataset, suggest that our system can anticipate quality issues with an accuracy of approximately 80%, enabling proactive defect prevention and a reduction in production losses. This approach demonstrates the potential for industrial AI applications for predictive quality assurance, highlighting the technical foundations and potential for industrial applications. Full article

Precise thermal management remains a critical challenge in Wire Arc-Direct Energy Deposition (W-DED) processes due to significant temperature fluctuations that can adversely impact part quality, dimensional accuracy, and process reliability. To address these issues, this study introduces a novel Hybrid Interlayer Hysteresis Controller (HIHC) designed specifically for W-DED, which integrates real-time thermal feedback and adaptive dwell time control. The system implements a dual-mode cooling strategy based on a temperature threshold, utilizing optical character recognition-based temperature monitoring and a rolling buffer system for stability. Experimental validation demonstrated improvements in thermal management, with the dynamic control system maintaining an average temperature undershoot of 1.38% while achieving 96.29% optimal temperature window compliance. Surface quality analysis revealed an 8.67% improvement in front face smoothness and a 5.15% enhancement in top surface quality. The dynamic control system also exhibited superior dimensional accuracy, producing thin walls with widths of 61.98 mm versus 66.43 mm in fixed dwell time samples, relative to the intended 60 mm specification. This study advances the field of additive manufacturing by establishing a robust framework for precise thermal management in W-DED processes, contributing to enhanced part quality, reduced post-processing requirements, and improved process reliability. Despite these advances, limitations include the system’s dependence on external optical monitoring hardware, potential scalability constraints for complex geometries, and limited testing across diverse material systems. Future work should focus on integrating multi-axis thermal sensors, extending the framework to multi-material deposition scenarios and implementing machine learning algorithms for predictive thermal modeling. Full article

Many countries around the world are in a race against time to decarbonise their energy systems. One of the avenues being explored in detail is Offshore Renewable Energy (ORE), with technologies such as wind, wave, and tidal. All of these technologies are in their infancy within the marine environment and required heavy Research and Development (R&D) to make them commercially viable. With so much demand for these industries, the supply chain is heavily constrained. A solution that has shown great potential to alleviate the pressure on the supply chain is the use of Wire Arc Additive Manufacturing (WAAM) for the use of onsite repair or manufacture for components. This is due to its ability to produce large-scale parts, with low emissions and at a lower cost than other Additive Manufacturing (AM) processes. The opportunity to use this technology could result in shorter downtimes and lead to a reduction in the Levelised Cost of Energy (LCOE). However, knowing that offshore structures are subject to cyclic loading conditions during their operational lifespan, fatigue properties of new materials and manufacturing processes must be well documented and studied to avoid any catastrophic failures. An issue often seen with WAAM is the presence of residual stresses. This study looks at fatigue cracking on Compact Tension C(T) specimens that have undergone laser shock peening and rolling, surface treatment processes that form compressive residual stresses at the surface of the material. In this study, the influence of fatigue overloading on the residual stress distribution in surface-treated WAAM specimens is evaluated and the effectiveness of the post-processing techniques on the subsequent fatigue behaviour is explored. Full article

The quality of the processed products in CNC machining centers is a critical factor in manufacturing equipment. The anomaly detection and predictive maintenance functions are essential for improving efficiency and reducing time and costs. This study aims to strengthen service competitiveness by reducing quality assurance costs and implementing AI-based predictive maintenance services, as well as establishing a predictive maintenance system for CNC manufacturing equipment. The proposed system integrates preventive maintenance, time-based maintenance, and condition-based maintenance strategies. Using continuous learning based on long short-term memory (LSTM), the system enables anomaly detection, failure prediction, cause analysis, root cause identification, remaining useful life (RUL) prediction, and optimal maintenance timing decisions. In addition, this study focuses on roller-cutting devices that are essential in packaging processes, such as food, pharmaceutical, and cosmetic production. When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments. Full article

The tissue paper converting system involves critical stages, including unwinding, embossing, folding, stacking, cutting, and packaging. Despite its maturity, challenges such as production interruptions during roll changes, equipment coordination issues, and unstable packaging speeds persist, affecting overall efficiency. While existing methods like fast roll change mechanisms and automated speed regulation systems have improved individual equipment performance, they fall short in achieving system-wide optimization. This paper proposes a digital-twin-based framework for the tissue paper converting system. High-precision simulation models are developed for key equipment, including the base paper roll stand, folding machine, paper stacker, and packaging machine, enabling real-time system monitoring and prediction. Key performance indicators such as base paper availability, folding speed, stacking status, and packaging progress are effectively predicted, preventing interruptions and reducing downtime. The proposed framework was validated in a real-world application at a Chinese paper manufacturing company. Results show that it increases machine runtime by 3 h per day, improving production efficiency by 15%. This leads to an additional daily output of 35,000 packs, contributing approximately CNY 85,000 in revenue growth. Additionally, the system helps prevent 300 kg of base paper waste daily, saving CNY 600 in manufacturing costs per day. Full article

Thin-film solar cells (TFSCs) represent a promising frontier in renewable energy technologies due to their potential for cost reduction, material efficiency, and adaptability. This literature review examines the key materials and advancements that make up TFSC technologies, with a focus on Cu(In,Ga)Se₂ (CIGS), cadmium telluride (CdTe), and Cu₂ZnSnS₄ (CZTS) and its sulfo-selenide counterpart Cu₂ZnSn(S,Se)₄ (CZTSSe). Each material’s unique properties—including tuneable bandgaps, high absorption coefficients, and low-cost scalability—make them viable candidates for a wide range of applications, from building-integrated photovoltaics (BIPV) to portable energy solutions. This review explores recent progress in the enhancement of power conversion efficiency (PCE), particularly through bandgap engineering, alkali metal doping, and interface optimization. Key innovations such as silver (Ag) alloying in CIGS, selenium (Se) alloying in CdTe, and sulfur (S) to Se ratio optimization in CZTSSe have driven PCE improvements and expanded the range of practical uses. Additionally, the adaptability of TFSCs for roll-to-roll manufacturing on flexible substrates has further cemented their role in advancing renewable energy adoption. Challenges remain, including environmental concerns, but ongoing research addresses these limitations, paving the way for TFSCs to become a crucial technology for transitioning to sustainable energy systems. Full article

In order to solve the problem of the insufficient vibration isolation performance of passenger cars in the suspension matching process, the six-degree-of-freedom (6-DOF) model, including three translational (x, y, z) and three rotational (roll, pitch, yaw) degrees of freedom, is established to comprehensively analyze the dynamic behavior of the powertrain mounting system. A 6-DOF dynamic model was established to analyze the decoupling rate and frequency distribution in its inherent characteristics, calculate the dynamic reaction of the suspension system, set the decoupling rate and the dynamic reaction of the suspension as optimization objectives, and use the NSGA II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm to optimize the stiffness of the suspension. The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. At the same time, this ensures that each suspension has enough static load support stiffness, and that its static deformation and amplitude are within the limit allowed under various working conditions, avoiding premature fatigue damage. The vibration isolation capability of the optimized system has been significantly improved, and the centroid acceleration has been significantly reduced under start–stop and road excitation conditions. The optimization method was effectively verified. Compared with existing studies focusing on single-objective optimization, the proposed NSGA II-based approach achieves a 93.4% decoupling rate in the critical Rx direction (vs. 59% pre-optimization) and reduces dynamic reaction forces by 8.3% (from 193 N to 177 N), demonstrating superior engineering applicability compared with traditional methods. Finally, the robustness analysis of the optimized stiffness met the requirements of production and manufacturing, indicating that the improvement of the decoupling rate of the suspension system and the optimization of the dynamic reaction force can effectively improve the vibration isolation performance, thereby improving the ride comfort of the vehicle. Full article