A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional(3D)network with dense microscopic open channels.The geometrical/morphological attributes,such as orientations,curva...A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional(3D)network with dense microscopic open channels.The geometrical/morphological attributes,such as orientations,curvatures and compactness,of fibers in the network is the key to the filtration performance of the material.However,most of the previous studies were based on materials’2D micro-images,which were unable to accurately measure these important 3D features of a filter’s structure.In this paper,we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images.Firstly,a series of images of the fiber assembly were captured at different depth layers as the stage moved vertically.Then a fusion image was established by extracting fiber edges from each layered image.Thirdly,the 3D coordinates of the fiber edges were determined using the sharpness/clarity of each edge pixel in the layered images.Finally,the 3D structure the fiber system was reconstructed through distance transformation based on the locations of fiber edges.展开更多
Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale s...Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.展开更多
The effects of Mn and Cr contents on bainitic transformation kinetics,microstructures and mechanical properties of high-carbon low alloy steels after austempered at 230,300 and 350 ℃ were determined by dilatometry,op...The effects of Mn and Cr contents on bainitic transformation kinetics,microstructures and mechanical properties of high-carbon low alloy steels after austempered at 230,300 and 350 ℃ were determined by dilatometry,optical microscopy,scanning electron microscopy,X-ray diffraction and tensile tests. The results showed that Mn and Cr can extend bainitic incubation period and completion time,and with the increase of Mn and Cr content,the bainitic ferrite plate thickness decreased and the volume fraction of retained austenite increased. TRIP（ transformation induced plasticity） effect was observed during tensile testing which improved the overall mechanical property. The increase of Mn concentration can improve the strength to a certain extent,but reduce the ductility. The increase of Cr concentration can improve the ductility of bainitic steels which transformed at a low temperature. The low temperature bainitic steel austempered at 230 ℃ exhibited excellent mechanical properties with ultimate tensile strength of（ 2146 ± 11） MPa and total elongation of（ 12. 95 ± 0. 15） %.展开更多
文摘A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional(3D)network with dense microscopic open channels.The geometrical/morphological attributes,such as orientations,curvatures and compactness,of fibers in the network is the key to the filtration performance of the material.However,most of the previous studies were based on materials’2D micro-images,which were unable to accurately measure these important 3D features of a filter’s structure.In this paper,we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images.Firstly,a series of images of the fiber assembly were captured at different depth layers as the stage moved vertically.Then a fusion image was established by extracting fiber edges from each layered image.Thirdly,the 3D coordinates of the fiber edges were determined using the sharpness/clarity of each edge pixel in the layered images.Finally,the 3D structure the fiber system was reconstructed through distance transformation based on the locations of fiber edges.
基金supported by the National Key R&D Program of China(2022ZD0117501)the National Natural Science Foundation of China(62201441)
文摘Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.
基金supported by the National Natural Science Foundation of China(Grant No.51271035 and U1560107)The financial support of the State Key Laboratory of Development and Application Technology of Automotive Steels
文摘The effects of Mn and Cr contents on bainitic transformation kinetics,microstructures and mechanical properties of high-carbon low alloy steels after austempered at 230,300 and 350 ℃ were determined by dilatometry,optical microscopy,scanning electron microscopy,X-ray diffraction and tensile tests. The results showed that Mn and Cr can extend bainitic incubation period and completion time,and with the increase of Mn and Cr content,the bainitic ferrite plate thickness decreased and the volume fraction of retained austenite increased. TRIP（ transformation induced plasticity） effect was observed during tensile testing which improved the overall mechanical property. The increase of Mn concentration can improve the strength to a certain extent,but reduce the ductility. The increase of Cr concentration can improve the ductility of bainitic steels which transformed at a low temperature. The low temperature bainitic steel austempered at 230 ℃ exhibited excellent mechanical properties with ultimate tensile strength of（ 2146 ± 11） MPa and total elongation of（ 12. 95 ± 0. 15） %.
