Search Results (469)

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

With new applications continuously emerging in the fields of manufacturing, quality control and inspection, the need to develop three-dimensional (3D) scanning solutions suitable for industrial environments increases. 3D scanning is the process of analyzing one or more objects in order to convert and store the object’s features in a digital format. Due to the increased costs of industrial 3D scanning solutions, this paper proposes an open-source, low-cost architecture for obtaining a 3D model that can be used in manufacturing, which involves a linear laser beam that is swept across the object via a rotating mirror, and a camera that grabs images, to further be used to extract the dimensions of the object through a technique inspired by laser triangulation. The 3D models for several objects are obtained, analyzed and compared to the dimensions of their respective real-world counterparts. For the tested objects, the proposed system yields a maximum mean height error of 2.56 mm, a maximum mean length error of 1.48 mm and a maximum mean width error of 1.30 mm on the raw point cloud and a scanning time of ∼4 s per laser line. Finally, a few observations and ways to improve the proposed solution are mentioned. Full article

This study investigates how economic development interacts with sustainability performance in the European Union, focusing on the structural and technological factors that shape progress in the green transition. Using Eurostat data for 27 EU member states over the period 2015–2023, the analysis employs panel econometric models (Pooled Ordinary Least Squares, Fixed Effects, and Random Effects) to explore how circular economy performance, innovation capacity, human capital, and renewable energy use influence environmental and economic outcomes across member states. The results show that R&D intensity and skilled human resources are key drivers of sustainability. Higher levels of circular material use and resource productivity contribute to long-term competitiveness. In contrast, uneven progress in renewable energy deployment points to persistent regional disparities and possible structural constraints that limit convergence. Northern and Western Europe record the strongest advances in innovation and environmental efficiency, whereas Southern and Eastern regions remain affected by industrial legacies and lower absorptive capacity. The findings highlight that, in the short term, renewable energy expansion may involve adjustment costs and potential trade-offs with economic competitiveness in less technologically developed economies. This study provides new comparative evidence on the differentiated pathways of the green transition across the EU. Policy implications suggest the need to reinforce R&D investment, expand circular manufacturing, and support an inclusive technological transition consistent with the European Green Deal and the United Nations 2030 Agenda. Full article

Functional steel surfaces engineered through tailored micro-nano structures are increasingly vital for various applications such as high-performance aerospace components, energy conversion systems and defense equipment. Femtosecond laser filament processing is a recently proposed remote fabrication technique, showing the capability of fabricating micro-nano structures on irregular and large-area surfaces without the need of tight focusing. Nevertheless, the mechanisms underlying the formation of filament-induced structures remain not fully understood. Here we systematically investigate the formation mechanisms of filament-induced micro-nano structures on stainless steel surfaces by processing stainless steel in three manners: point, line, and area. We clarify the decisive role of the unique core–reservoir energy distribution of the filament in the formation of filament-induced micro-nano structures, and reveal that ablation, molten metal flow, and metal vapor condensation jointly drive the structure evolution through a dynamic interplay of competition and coupling, giving rise to the sequential morphological transitions of surface structures, from laser-induced periodic surface structures to ripple-like, crater-like, honeycomb-like, and ultimately taro-leaf-like structures. Our work not only clarifies the mechanisms of femtosecond laser filament processed morphological structures on steels but also provides insights onto intelligent manufacturing and design of advanced functional steel materials. Full article

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

The irrigation sector urgently needs more eco-sustainable materials able to guarantee the same performance as traditional fittings manufactured from virgin fossil-based polymers. In this study, sustainable composites were developed by melt-compounding virgin and recycled polypropylene (RPP) with hazelnut shell (HS) powder with or without maleic-anhydride-grafted polypropylene (PPC) coupling agent. The materials were characterized by a rheological and mechanical point of view. At high shear rates, the viscosity curves of matrices and composites converge, making the difference between neat and filled systems negligible in terms of processability. This indicates that standard injection-molding parameters used for the neat matrices can also be applied to the composites without significant adjustments. Tensile tests showed that adding 10 wt% HS powder increased the elastic modulus by approximately 30% (from 960 MPa to 1.2 GPa) while reducing elongation at break by about 90% compared with neat RPP. The use of PPC mitigated this loss of ductility, partially restoring tensile strength and increasing EB from 6% to 18% in RPP-based composites (+200%). Finally, sleeve bodies and nuts injection-molded from RPP/HS5 and RPP/HS5/PPC successfully resisted internal water pressure up to 3.5 bar without leakage or structural damage. These findings demonstrate that agro-industrial waste can be effectively valorized as a functional filler in recycled polypropylene, enabling the manufacture of irrigation fittings with mechanical and processing performances comparable to those of virgin PP and supporting the transition toward a circular economy. Full article

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use. Full article

With the rapid development of infrastructure and the need to protect natural ecosystems, manufactured sand is used to replace river sand in concretes. To compare the deterioration patterns of concretes made with different sands, C50 specimens using basalt (C50X), tuff (C50N), and granite (C50H) manufactured sands and river sand (C50T) were prepared, then tested outdoors by full burial in a sulfate saline soil and indoors by accelerated freeze–thaw in a sulfate solution. The outdoor experiments indicate that C50X deteriorated the slowest, whereas the resistance to mass loss ranking was: C50X > C50H > C50N > C50T. In the indoor freeze–thaw experiments, C50X also performed best, retaining 51% relative dynamic modulus of elasticity (RDME) after 450 cycles. X-ray diffraction and scanning electron microscopy showed that C50T was weakened by abundant MgSO₄·7H₂O crystals, while C50X formed a denser matrix that limits salt-crystallization expansion. Moreover, a GM(1,1)-Markov model was developed to forecast long-term durability. For C50X, the model predicted an estimated service life of 68 months in the outdoor environment, at which point it is projected to reach the 5% mass loss failure threshold. Separately, it forecasted that the RDME would remain above 41% after 450 indoor freeze–thaw cycles. Full article

Transparent CAD/CAM monolithic ceramics are increasingly used in dentistry due to their combination of high strength, esthetics, and durability, achieved through high yttria content and multilayered systems. This study evaluates the mechanical behavior of four widely used CAD/CAM ceramics, correlating their performance with microstructural characteristics. Bar-shaped specimens (n = 10 per material, for each test) of ZOLID^® FX ML (ZF), IPS E.MAX^® CAD (MC), E.MAX^® ZIRCAD (ZM), and KAT-ANA^® STML (KS) (all A2 shade) were prepared and sintered according to manufacturers’ protocols. Flexural strength and elastic modulus were measured using three-point bending, and Vickers hardness was determined separately. Statistical normality was confirmed with the Kolmogorov–Smirnov test. Flexural strength ranged from 252.8 ± 39.8 MPa (MC) to 547.6 ± 125.7 MPa (ZM), elastic modulus from 65.8 ± 6.5 GPa (MC) to 94.1 ± 5.8 GPa (KS), and hardness from 4.2 ± 0.2 GPa (MC) to 9.6 ± 0.6 GPa (ZF). High-elastic-modulus materials (KS, ZM) can better resist deformation under occlusal loads, improving long-term stability of posterior crowns, bridges, and implant-supported restorations. High hardness (ZF) provides superior wear resistance and preserves occlusal anatomy over time, making it suitable for thin-shell restorations and high-stress functional surfaces. Materials with lower modulus and hardness (MC) are more suitable for intra-coronal restorations or thin veneers where stress shielding and material compliance are advantageous. These findings support material selection based on mechanical demands, and further clinical studies are needed to confirm long-term performance. Full article

Large-format additive manufacturing (LFAM) is an enabling manufacturing technology capable of producing large parts with highly complex geometries for a wide variety of applications, including automotive, infrastructure/construction, and aerospace mold and tooling. In the past decade, the LFAM industry has seen widespread use of bio-based, glass, and/or carbon fiber reinforced thermoplastic composites which, when printed, serve as a lower-cost alternative to metallic parts. One of the highest-volume materials utilized by the industry is carbon fiber (CF)-filled polycarbonate (PC), which in out-of-autoclave applications can achieve comparable mechanical performance to metal at a significantly lower cost. Previous work has shown that if this material is recovered at various points throughout the manufacturing process for both the lab and pilot scale, it can be mechanically recycled with minimal impacts on the functional performance and printability of the material while significantly reducing the feedstock costs. End-of-life (EOL) CF-PC components were processed through industrial shredding, melt compounding, and LFAM equipment, followed by evaluation of the second-life material properties. Experimental assessments included quantitative analysis of fiber length attrition, polymer molecular weight degradation using gel permeation chromatography (GPC), density changes via pycnometry, thermal performance using dynamic mechanical analysis (DMA), and mechanical performance (tensile properties) in both the X- and Z-directions. Results demonstrated a 24.6% reduction in average fiber length compared to virgin prints, accompanied by a 21% decrease in X-direction tensile strength and a 39% reduction in tensile modulus. Despite these reductions, Z-direction tensile modulus improved by 4%, density increased by 6.8%, and heat deflection temperature (HDT) under high stress retained over 97% of its original value. These findings underscore the potential for integrating mechanically recycled CF-PC into industrial LFAM applications while highlighting the need for technological innovations to mitigate fiber degradation and enhance material performance for broader adoption. This critical step toward circular material practices in LFAM offers a pathway to reducing feedstock costs and environmental impact while maintaining functional performance in industrial applications. Full article

Despite the digital era and the emergence of simplified production paradigms enabled by digital twin technology, traditional manufacturing processes like lathe machining are invaluable because of their ability to meet various and multiple needs reliably and flexibly. To combine these processes into smart monitoring and control systems, including tool wear detection and fault diagnosis, precise multi-axis force measurement is necessary. Single-axis sensors are insufficient to measure the entire dynamics of cutting interactions. A new idea based on strain gauge technology for a self-decoupled three-axis force sensor is proposed in this paper, which aims to measure cutting forces and vibration in a lathe operation. During the cutting process, the sensor is designed to independently recognize the onset of forces at three orthogonal axes to enhance real-time process monitoring capabilities. The Timoshenko beam theory is used to design the mechanical structure, where sensitivity could be improved with minimal crosstalk. Finite element analysis (FEA) simulations were conducted to evaluate the sensor’s performance, stress distribution, modal assessments, and interference error. The interference error of 0.31 percent indicated by the results of the simulation is extremely low, indicating a successful decoupling of the force components. These encouraging simulation results indicate that there is a high possibility of applying the proposed sensor design to intelligent manufacturing systems. It presents an initial point of departure into the embodiment of sophisticated monitoring platforms for conventional machining processes, eliminating the discontinuity between old and new smart manufacturing systems. Full article

Line heating processes play a significant role in the fabrication of structural steel components, particularly in industries such as shipbuilding, aerospace, and automotive manufacturing, where dimensional accuracy and minimal defects are critical. Traditional methods, such as the finite element method (FEM) simulations, offer high-fidelity predictions but are hindered by prohibitive computational latency and the need for case-specific re-meshing. This study presents a physics-aware, data-driven neural network that delivers fast, high-fidelity temperature predictions across a broad operating envelope. Each spatiotemporal point is mapped to a one-dimensional feature vector. This vector encodes thermophysical properties, boundary influence factors, heatsource variables, and timing variables. All geometric features are expressed in a path-aligned local coordinate frame, and the inputs are appropriately normalized and nondimensionalized. A lightweight multilayer perceptron (MLP) is trained on FEM-generated induction heating data for steel plates with varying thickness and randomized paths. On a hold-out test set, the model achieves MAE = 0.60 °C, RMSE = 1.27 °C, and R2 = 0.995, with a narrow bootstrapped 99.7% error interval (−0.203 to −0.063 °C). Two independent experiments on an integrated heating and mechanical rolling forming (IHMRF) platform show strong agreement with thermocouple measurements and demonstrate generalization to a plate size not seen during training. Inference is approximately five orders of magnitude (~10⁵) faster than FEM, enabling near-real-time full-field reconstructions or targeted spatiotemporal queries. The approach supports rapid parameter optimization and advances intelligent line heating operations. Full article

The detection and monitoring of ions are essential for a broad range of applications, including industrial process control and biomedical diagnostics. Traditional ion-sensitive field-effect transistors require bulky and expensive reference electrodes, which face several limitations, including device miniaturization, high fabrication costs, and incompatibility with semiconductor manufacturing processes. Here, we introduce a reference-less semiconductor ion sensor (RELESIS) that utilizes anodic aluminum oxide film as both the sensitive and dielectric layer. The RELESIS is composed of a metal-oxide-semiconductor field-effect transistor and an interdigital electrode, which fundamentally eliminates the need for a reference electrode, thereby enabling device miniaturization. During fabrication, the anodic oxidation process is employed in place of the expensive atomic layer deposition method, significantly reducing manufacturing costs while maintaining high surface quality. In practical measurements, the RELESIS device demonstrated an excellent pH sensitivity of 57.8 mV/pH with a low hysteresis of 7 mV. As a proof-of-concept application, the RELESIS device was employed for real-time, non-destructive monitoring of milk freshness, accurately detecting pH changes from fresh to spoiled in milk samples. The combination of reference-less structure, low-cost fabrication, and superior sensing performance positions this technology as a promising platform for next-generation portable ion sensing systems in food safety, environmental monitoring, and point-of-care diagnostics. Full article

An integrated ESP32-based measurement system called PV-Scope is presented for real-time photovoltaic (PV) module efficiency characterization and small off-grid system testing under field conditions. The system includes pyranometer-calibrated irradiance sensors using a solar simulator, maximum power point tracking, and comprehensive environmental monitoring to enable accurate performance assessment of PV modules across diverse technologies, manufacturers and installation conditions. Unlike standard test condition (STC) measurements at cell temperatures of 25 °C, this system captures the interactions between efficiency and environmental variables that significantly impact real-world efficiency. In particular, measurement of temperature-dependent efficiency under local conditions and validation of temperature-dependent models for extending the results to other environmental conditions are enabled with cell temperature monitoring in addition to ambient temperature, humidity, and wind speed. PV-Scope is designed for integrated sensing versatility, portable outdoor testing, and order-of-magnitude cost savings compared to commercial equipment to meet measurement needs across research, education, and practical PV innovation, including bifacial module testing, assessment of cooling techniques, tandem and multi-junction testing, and agrivoltaics. Full article