Low reactivity and appropriate wettability between molten superalloys and ceramic materials are crucial for the production of high-quality superalloy castings.The sessile-drop experiment was employed to systematically...Low reactivity and appropriate wettability between molten superalloys and ceramic materials are crucial for the production of high-quality superalloy castings.The sessile-drop experiment was employed to systematically investigate the interfacial reaction and wettability between the 4777DS1 superalloy and SiO_(2)-based ceramic core at various temperatures(1,480℃,1,500℃,1,520℃,and 1,550℃).The wetting behavior and interfacial reaction products at different temperatures were analyzed by scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and X-ray diffraction(XRD).The interfacial reaction process and products were discussed,and the thermodynamic behavior and interfacial reaction mechanisms were elucidated.The results demonstrate that the wetting behavior and interfacial reaction between the 4777DS1 alloy and the ceramic core are significantly influenced by temperature.The wettability angle exhibits a trend of initial decrease followed by an increase with rising temperature,reaching a maximum of 139°at 1,480℃,indicating poorer wettability of the 4777DS1 superalloy with the ceramic core and better casting properties at this specific temperature.The most intense interfacial reaction occurs at 1,520℃,resulting in the formation of the main interfacial reaction products such as Al_(2)O_(3),SiO_(2),and HfO_(2).Additionally,some crystal-like products rich in Si and Hf distribute on the reaction layer.展开更多
Super-resolution(SR)reconstruction addresses the challenge of enhancing image resolution,which is critical in domains such as medical imaging,remote sensing,and computational photography.High-quality image reconstruct...Super-resolution(SR)reconstruction addresses the challenge of enhancing image resolution,which is critical in domains such as medical imaging,remote sensing,and computational photography.High-quality image reconstruction is essential for enhancing visual details and improving the accuracy of subsequent tasks.Traditional methods,including interpolation techniques and basic CNNs,often fail to recover fine textures and detailed structures,particularly in complex or high-frequency regions.In this paper,we present Deep Supervised Swin Transformer U-Net(DSSTU-Net),a novel architecture designed to improve image SR by integrating Residual Swin Transformer Blocks(RSTB)and Deep Supervision(DS)mechanisms into the U-Net framework.DSSTU-Net leverages the Swin Transformer’s multi-scale attention capabilities for robust feature extraction,while DS at various stages of the network ensures better gradient propagation and refined feature learning.The ST block introduces a hierarchical self-attention mechanism,allowing the model to capture both local and global context,which is crucial for high-quality SR tasks.Moreover,DS applied at multiple layers in the decoder enables direct supervision on intermediate feature maps,accelerating convergence and improving performance.The DSSTU-Net architecture was rigorously evaluated on the DIV2K,LSDIR,SET5,and SET14 datasets,demonstrating its superior ability to generate high-quality images.Furthermore,the potential applications of this model extend beyond image enhancement,with promising use cases in medical imaging,satellite imagery,and industrial inspection,where high-quality image reconstruction plays a crucial role in accurate diagnostics and operational efficiency.This work provides a reference method for future research on advanced image restoration techniques.展开更多
基金supported by the fund of State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment(No.DEC8300CG202210353EE280297)the China Postdoctoral Science Foundation(No.2021M692555)+1 种基金the Shaanxi Province Qinchuangyuan‘Scientists+Engineers’Team Building Project(No.2023KXJ-266)the Fundamental Research Funds for the Central Universities(No.xzy012023145)。
文摘Low reactivity and appropriate wettability between molten superalloys and ceramic materials are crucial for the production of high-quality superalloy castings.The sessile-drop experiment was employed to systematically investigate the interfacial reaction and wettability between the 4777DS1 superalloy and SiO_(2)-based ceramic core at various temperatures(1,480℃,1,500℃,1,520℃,and 1,550℃).The wetting behavior and interfacial reaction products at different temperatures were analyzed by scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and X-ray diffraction(XRD).The interfacial reaction process and products were discussed,and the thermodynamic behavior and interfacial reaction mechanisms were elucidated.The results demonstrate that the wetting behavior and interfacial reaction between the 4777DS1 alloy and the ceramic core are significantly influenced by temperature.The wettability angle exhibits a trend of initial decrease followed by an increase with rising temperature,reaching a maximum of 139°at 1,480℃,indicating poorer wettability of the 4777DS1 superalloy with the ceramic core and better casting properties at this specific temperature.The most intense interfacial reaction occurs at 1,520℃,resulting in the formation of the main interfacial reaction products such as Al_(2)O_(3),SiO_(2),and HfO_(2).Additionally,some crystal-like products rich in Si and Hf distribute on the reaction layer.
基金supported in part by the National Natural Science Foundation of China(62263006)2021 Director’s Fund of the Guangxi Key Laboratory for Automatic Detection Technology and Instruments(YQ21107)+2 种基金Guilin University of Electronic Technology Scientific Research Fund Project(UF24014Y)Innovation Project of Guangxi Graduate Education(YCSW2024336)Middle-aged and Young Teachers’Basic Ability Promotion Project of Guangxi(2021KY0802).
文摘Super-resolution(SR)reconstruction addresses the challenge of enhancing image resolution,which is critical in domains such as medical imaging,remote sensing,and computational photography.High-quality image reconstruction is essential for enhancing visual details and improving the accuracy of subsequent tasks.Traditional methods,including interpolation techniques and basic CNNs,often fail to recover fine textures and detailed structures,particularly in complex or high-frequency regions.In this paper,we present Deep Supervised Swin Transformer U-Net(DSSTU-Net),a novel architecture designed to improve image SR by integrating Residual Swin Transformer Blocks(RSTB)and Deep Supervision(DS)mechanisms into the U-Net framework.DSSTU-Net leverages the Swin Transformer’s multi-scale attention capabilities for robust feature extraction,while DS at various stages of the network ensures better gradient propagation and refined feature learning.The ST block introduces a hierarchical self-attention mechanism,allowing the model to capture both local and global context,which is crucial for high-quality SR tasks.Moreover,DS applied at multiple layers in the decoder enables direct supervision on intermediate feature maps,accelerating convergence and improving performance.The DSSTU-Net architecture was rigorously evaluated on the DIV2K,LSDIR,SET5,and SET14 datasets,demonstrating its superior ability to generate high-quality images.Furthermore,the potential applications of this model extend beyond image enhancement,with promising use cases in medical imaging,satellite imagery,and industrial inspection,where high-quality image reconstruction plays a crucial role in accurate diagnostics and operational efficiency.This work provides a reference method for future research on advanced image restoration techniques.
