Search Results (142)

Most aircraft skin components are typical sheet metal components, and stretch forming serves as the primary forming process. The die face is the core foundation for both the finite element simulation and mold trial. Due to the intricate geometric characteristics of aircraft skin components and iterative revisions caused by stretch forming process adjustments and product design changes, the die face design of aircraft skin components is inherently time-intensive, highly complex, and prone to instability. To address these issues, a Hierarchical and Hybrid Association Method (HHAM) based on a robust updating mechanism and hybrid associations is proposed for the intelligent design system. HHAM can significantly enhance the stability and efficiency of die face design. Specifically, the hierarchical and automatic updating process of HHAM, incorporating robust error handling mechanisms, is the core methodology that guarantees the stability of complex and iterative die face design for aircraft skin. Moreover, the inter-module hybrid association, which integrates parametric modeling and automatic connection techniques, eliminates the instability in die face design updating caused by feature and topology variations. Additionally, robust geometric algorithms for wireframe modeling effectively improve the surface quality and generation success rate of the die face. The intelligent design system developed based on the CATIA platform has been successfully applied in two professional aircraft skin component manufacturing enterprises. Case studies and industrial application practices verify the effectiveness of the proposed system, achieving a 72.7% improvement in design efficiency and a 70.27% reduction in the risk of die face update errors. Full article

Electron transfer (ET) is a foundational biogeochemical process in paddy soils, distinctively molded by alternating anaerobic-aerobic conditions from flooding-drainage cycles. Despite extensive research on heavy metal(loid) (denoted as “HM”, e.g., As, Cd, Cr, Hg) dynamics in paddies, ET has not been systematically synthesized as a unifying regulatory mechanism, and the trade-offs of ET-based mitigation strategies remain unclear. These critical gaps have drastically controlled HMs’ mobility, which further modulates bioavailability and subsequent accumulation in rice (Oryza sativa L., a staple sustaining half the global population), posing substantial food safety risks. Alongside progress in electroactive microorganism (EAM) research, extracellular electron transfer (EET) mechanism delineation, and soil electrochemical monitoring, ET’s role in orchestrating paddy soil HM dynamics has garnered unparalleled attention. This review explicitly focuses on the linkage between ET processes and HM biogeochemistry in paddy ecosystems: (1) elucidates core ET mechanisms in paddy soils (microbial EET, Fe/Mn/S redox cycling, organic matter-mediated electron shuttling, rice root-associated electron exchange) and their acclimation to flooded conditions; (2) systematically unravels how ET drives HM valence transformation (e.g., As(V) to As(III), Cr(VI) to Cr(III)), speciation shifts (e.g., exchangeable Cd to oxide-bound Cd), and mobility changes; (3) expounds on ET-regulated HM bioavailability by modulating soil retention capacity and iron plaque formation; (4) synopsizes ET-modulated HM accumulation pathways in rice (root uptake, xylem/phloem translocation, grain sequestration); (5) evaluates key factors (water management, fertilization, straw return) impacting ET efficiency and associated HM risks. Ultimately, we put forward future avenues for ET-based mitigation strategies to uphold rice safety and paddy soil sustainability. Full article

Low-melting bioabsorbable polymers, such as poly(caprolactone-co-lactide) (PCLA), hold significant promise for biomedical applications. However, achieving high-precision micro- and nanotopographical functionalization remains a formidable challenge due to the material’s susceptibility to thermal deformation during conventional thermal molding processes. In this study, functional microstructured PCLA coatings were engineered via low-temperature nanoimprint lithography utilizing a TiO₂–SiO₂ gas-permeable mold. These molds were synthesized via a sol–gel method utilizing titanium dioxide and silicon precursors. The gas-permeable nature of the mold facilitated the efficient evacuation of trapped air and volatiles during the imprinting process, enabling the high-fidelity replication of microstructures (1.3 μm height, 3 μm pitch) and nanostructured PCLA coatings featuring linewidths as narrow as 600 nm. The resultant microstructured PCLA coatings demonstrated modulated surface wettability, evidenced by an increase in water contact angles from 70.1° to 91.4°, and exhibited enhanced FD4 elution kinetics. These results confirm morphology-driven functionalities, specifically hydrophobicity and controlled release capabilities. Collectively, these findings underscore the efficacy of this microfabrication approach for polycaprolactone-based materials and highlight its potential to catalyze the development of high-value-added biomaterials for advanced medical and life science applications. This study establishes a foundational framework for the practical deployment of next-generation bioabsorbable materials and is anticipated to drive innovation in precision medical manufacturing. Full article

In this study, we aimed to elucidate the mechanism through which treatment with slight water loss combined with methyl jasmonate (MeJA) regulates gray mold development in Actinidia arguta, focusing on reactive oxygen species (ROS) and phenylpropanoid metabolism. The results showed that water loss alone, MeJA alone, and their combination each reduced the incidence of disease, with the combined treatment showing the greatest efficacy. At the end of the storage period, the combined treatment enhanced the activities of superoxide dismutase (SOD), polyphenol oxidase (PPO), peroxidase (POD), phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). It also increased the accumulation of defense-related substances (total phenol and lignin contents) and up-regulated AaPAL, Aa4CL, AaC4H, and AaC3′H gene expression. Furthermore, the combined treatment reduced the disease severity index from 60% to 16% and delayed onset by 2 d. In conclusion, slight water loss combined with MeJA treatment effectively suppressed gray mold. This effect may be attributed to activation of ROS metabolism, induction of phenylpropanoid metabolism, and up-regulation of related genes, which enhanced the resistance of the fruit to gray mold. Full article

In this study, the thermal and structural behavior of battery module components produced from polymer-based composites was systematically evaluated using coupled Moldflow 2016 and ANSYS Fluent 2024 simulations. Three thermoplastics—metal-flake-reinforced PC+ABS (Polycarbonate/Acrylonitrile Butadiene Styrene), carbon-fiber-reinforced PEEK (Polyether Ether Ketone), and hybrid mineral-filled PP (Polypropylene)—were investigated as alternatives to conventional aluminum components. Moldflow simulations enabled the assessment of injection molding performance by determining injection pressure, volumetric shrinkage, warpage, residual stress, flow front temperature, and part weight. PEEK exhibited the best dimensional stability, with minimal warpage and shrinkage, while PP showed significant thermomechanical distortion, indicating poor resistance to thermally induced deformation. For thermal management, steady-state simulations were performed on a 1P3S pouch cell battery configuration using the NTGK/DCIR model under a constant heat load of 190 W. Material properties, including temperature-dependent thermal conductivity, density, and specific heat capacity, were defined based on validated databases. The results revealed that temperature distribution and Joule heat generation were strongly influenced by thermal conductivity. While aluminum exhibited the most favorable thermal dissipation, PC+ABS closely matched its electrical performance, with only a 1.3% lower average current magnitude. In contrast, PEEK and PP generated higher cell core temperatures (up to 20 K) due to limited heat conduction, although they had comparable current magnitudes imposed by the energy-conserving model. Overall, the findings indicate that reinforced thermoplastics, particularly PC+ABS, can serve as lightweight and cost-effective alternatives to aluminum in mid-range battery modules, providing similar electrical performance and thermal losses within acceptable limits. Full article

Ancient paintings, as invaluable cultural heritage, often suffer from damages like creases, mold, and missing regions. Current restoration methods, while effective for natural images, struggle with the fine-grained control required for ancient paintings’ artistic styles and brushstroke patterns. We propose the Semantic and Sketch-Guided Restoration (SSGR) framework, which uses pixel-level semantic maps to restore missing and mold-affected areas and depth-aware sketch maps to ensure texture continuity in creased regions. The sketch maps are automatically extracted using advanced methods that preserve original brushstroke styles while conveying geometry and semantics. SSGR employs a semantic segmentation network to categorize painting regions and depth-sensitive sketch extraction to guide a diffusion model. To enhance style controllability, we cluster diverse attributes of landscape paintings and incorporate a Semantic-Sketch-Attribute-Normalization (SSAN) block that explores consistent patterns across styles through spatial semantic and attribute-adaptive normalization modules. Evaluated on the CLP-2K dataset, SSGR achieves an mIoU of 53.30%, SSIM of 0.42, and PSNR of 13.11, outperforming state-of-the-art methods. This approach not only preserves historical aesthetics but also advances digital heritage preservation with a tailored, controllable technique for ancient paintings. Full article

Recently, the problem of climate change caused by the increase in greenhouse gases has become a major issue, and the importance of eco-friendly energy is increasing worldwide. The installation of PV systems is increasing, and they are being installed in areas adjacent to users. Nevertheless, the concerns relevant to safety problems, such as grounding modules to prevent electric shock accidents, should be addressed. This study examined the effectiveness of applying grounding clips for PV module installations. When the grounding clip was applied, it showed approximately 6.7% improvement compared to the resistance value of the existing grounding wire. The grounding performance and construction convenience of the technology applied with the new grounding clip were verified by a comparison with the existing conventional grounding wire. For manufacturing the clips, a mold with a 32° cone angle and a height of 3.5 mm was used, and a fastening torque of 225 kgf∙cm was found to achieve satisfactory grounding resistance values compared to the conventional approach. Using a power tool and expanding it to nine modules (=5.56 kWp), the clip installation process took 585 s, which succeeded in reducing the installation time by approximately 38.1% compared to the 945 s taken using wires. Moreover, the module could be installed and grounded with only a two-step process of installing the module and fastening it with bolts, increasing the installation economy. Full article

Tomato is a key economic crop whose yield and quality depend heavily on the early and accurate detection of leaf diseases. Conventional diagnosis based on manual observation is labor-intensive and prone to subjective bias. To overcome the limitations of disease detection under complex environmental conditions, this study presents an enhanced YOLO11n-based detection framework for tomato leaf diseases. The proposed model integrates an EfficientMSF module in the backbone to strengthen multi-scale feature extraction, introduces a C2CU module to enhance global contextual representation, and employs a CAFMFusion module to achieve efficient fusion of local and global features. Experiments were conducted on a self-constructed dataset containing nine tomato leaf categories, including eight disease types and healthy samples. The proposed approach achieves an average Recall of 71.0%, mAP@0.5 of 76.5%, and mAP@0.5–0.95 of 60.5%, outperforming the baseline YOLO11n by 3.4%, 1.3%, and 2.0%, respectively. In particular, for the challenging Leaf Mold class, mAP@0.5 improved by 3.4%. These results demonstrate that the proposed method possesses strong robustness and practical applicability in complex field conditions, offering an effective solution for intelligent tomato disease monitoring and precision agricultural management. Full article

This study investigates the mechanical, thermal, electrical, and microstructural properties of polyamide 66 (PA66) composites reinforced with waste-derived micro–nano NiCr (80/20) powders. Composites containing 2, 5, and 8 wt% NiCr were prepared using thermokinetic mixing and compression molding, followed by characterization via tensile testing, Shore D hardness, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), and thermal/electrical conductivity measurements. Results showed a progressive increase in tensile modulus, tensile strength, hardness, and thermal conductivity with increasing NiCr content, reaching maximum values at 8 wt% filler. However, elongation at break decreased, indicating reduced ductility due to restricted polymer chain mobility. DSC and FTIR analyses revealed that low NiCr loadings promoted nucleation and crystallinity, while higher contents disrupted crystalline domains. Electrical conductivity exhibited a slight upward trend, remaining sub-percolative up to 8 wt% NiCr; conductivity modulation is modest at high filler loadings. SEM–EDS confirmed uniform dispersion at low–moderate contents and agglomeration at higher levels. The use of industrial waste NiCr powder not only enhanced material performance but also contributed to sustainable materials engineering by valorizing by-products from the coatings industry. These findings suggest that NiCr/PA66 composites have potential applications in automotive, electronics, and thermal management systems requiring improved mechanical rigidity and heat dissipation. Full article

A critical aspect in the design of power electronics packages is the prediction of their mechanical response under severe thermomechanical loads and the consequent structural damage. For this purpose, finite element (FE) simulations are used to estimate the mechanical performance and reliability under operational conditions, typically alternate high voltages/currents resulting in thermal gradients. When simulations are performed, it is common practice to consider the as-received package to be in a stress-free state. Namely, residual stresses and plastic deformation induced by the manufacturing processes are neglected. In this study, an advanced FE modeling approach is proposed to assess the structural consequences of the encapsulating resin curing, typical in the production of silicon carbide (SiC)-based power electronics modules for electric vehicles. This work offers a general modeling framework that can be further employed to simulate the effects of thermal gradients induced by the production process on the effective shape and residual stresses of the as-received package for other manufacturing stages, such as metal brazing, soldering processes joining copper and SiC, and, to lower extents, the application of polyimide on top of passivation layers. The obtained results have been indirectly validated with experimental data from literature. Full article

This article presents the technology of automated placement of an injection molding gate based on a parametric optimization algorithm with technological constraints consideration. The algorithm is based on the modification of the genetic algorithm using the criterion of maximum equivalent stresses on the weld line as an optimization criterion. The proposed software’s modular structure combines the authors’ modules that implement a new optimization algorithm with the ANSYS 2022R1 and Moldflow calculation kernels called via API interfaces. This structure provides an opportunity to implement developed technology to solve industrial problems using standard mesh generation tools and complex geometric models due to the flexibility of modules and computing kernel scalability. The consideration of the technological constraints allows us to reduce the population size and optimization problem solution computational time to 1.9 times. The developed algorithms are used to solve the gate location optimization problem using the example of an aerospace bracket made of short-reinforced composite material with a nonzero genus surface and a weld line. The use of the proposed technology made it possible to increase the strength of the studied structure by two times. Full article

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics. Full article

This study aims to develop egg white protein (EWP) biocomposite films reinforced with chitin nanocrystals (ChNCs, 1–5 wt.%) by compression molding to overcome the mechanical and barrier limitations of protein-based films for sustainable packaging. ChNC incorporation may modulate film microstructure, crystallinity, and thermal stability, thereby enhancing functional performance. Films were prepared by adding ChNCs either as aqueous suspensions or lyophilized powder, and their structural, thermal, mechanical, optical, and barrier properties were systematically evaluated. Scanning electron microscopy confirmed a more homogeneous dispersion of ChNCs when added as suspensions, while powder addition promoted partial aggregation. X-ray diffraction revealed increased crystallinity with ChNC reinforcement. Mechanical tests showed that films with 2 wt.% ChNCs in suspension exhibited the highest tensile strength, whereas those with 5 wt.% in powder form became stiffer but less extensible. Oxygen permeability was not significantly affected, while water vapor permeability decreased by up to 14.5% at 2 wt.% ChNCs incorporated as powder. Transparency and color remained largely unchanged by ChNC addition, except for a slight increase in yellowness. Overall, these findings demonstrate that the incorporation method and concentration of ChNCs play a crucial role in tailoring the physicochemical performance of EWP films. The results provide new insights into the design of EWP-based nanocomposites and support their potential as bio-derived materials for advanced food packaging applications. Full article

Strawberry, as an important global economic crop, its disease prevention and control directly affects yield and quality. Traditional detection means rely on manual observation or traditional machine learning algorithms, which have defects such as low efficiency, high false detection rate, and insufficient adaptability to tiny disease spots and complex environment. To solve the above problems, this study proposes a strawberry disease recognition method based on improved YOLOv8. By systematically acquiring 3146 image data covering seven types of typical diseases, such as gray mold and powdery mildew, a high-quality dataset containing different disease stages and complex backgrounds was constructed. Aiming at the difficulties in disease detection, the YOLOv8 model is optimized in two stages: on the one hand, the ultra-small scale detection head (32 × 32) is introduced to enhance the model’s ability to capture early tiny spots; on the other hand, the convolution and attention fusion module (CAFM) is combined to enhance the feature robustness in complex field scenes through the synergy of local feature extraction and global information focusing. Experiments show that the mAP50 of the improved model reaches 0.96 and outperforms mainstream algorithms such as YOLOv5 and Faster R-CNN in both recall and F1 score. In addition, the interactive system developed based on the PyQT5 framework can process images, videos and camera inputs in real time, and the disease areas are presented intuitively through visualized bounding boxes and category labels, which provides farmers with a lightweight and low-threshold field management tool. This study not only verifies the effectiveness of the improved algorithm but also provides a practical reference for the engineering application of deep learning in agricultural scenarios, which is expected to promote the further implementation of precision agriculture technology. Full article

This study optimizes the design and fabrication of an injection-molded Alvarez freeform lens using Moldex3D mold flow analysis and CODE V optical design simulations. The dual-software approach facilitates the transition between the manufacturing simulations and the optical design/verification process, thereby addressing the conversion issues between the two analysis modules. The optical quality of the designed lens is evaluated using spot diagram, distortion, and modulation transfer function (MTF) simulations. The Taguchi design methodology is first employed to identify the individual effects of the key injection molding parameters on the quality of the fabricated lens. The quality is then further improved by utilizing two multi-objective optimization methods, namely Gray Relational Analysis (GRA) and Robust Multi-Criteria Optimization (RMCO), to determine the optimal combination of the injection molding parameters. The results demonstrate that RMCO outperforms GRA, showing more substantial improvements in the optical quality of the lens. Overall, the proposed integrated method, incorporating Moldex3D, CODE V, Taguchi robust design, and RMCO analyses, provides an effective approach for optimizing the injection molding of Alvarez freeform lenses, thereby enhancing their quality. Future research could extend this methodology to other optical components and more complex optical systems. Full article