非晶合金宇航服役热环境下蠕变行为研究

通过宏观尺度蠕变实验,在宇航服役环境范围内,对锆基非晶合金蠕变行为进行了研究。结果表明,在200℃时,非晶合金未发生显著的压缩蠕变,其长期变形比率与自由体积的增值数值变化存在表观一致,说明自由体积作为孕育形成剪切带的初始结构单元,在非晶合金蠕变变形中具有重要作用。在300℃时,非晶合金在低于屈服强度和玻璃转变温度的条件下,发生了显著的压缩和拉伸蠕变,实验结果给出了在宇航热环境下非晶合金的可靠服役热环境边界和载荷包络。锆基非晶合金服役温度不超过200℃,载荷不超过1000 MPa。     

Fabrication and investigation of Ca/Tb co-doped HfO2 infrared coatings

In this study, Ca/Tb co-doped HfO₂ coatings were prepared by atmosphere plasma spraying. The chemical composition, morphology and infrared property of the coatings were characterized. The coatings possessed a layer-stacked morphology. When the Ca/Tb doping atomic ratio was 1:1, the phase of the coatings gradually changed from monoclinic to cubic with increasing the doping mass. The CTH2 coating had the highest emissivity which was 0.820 in 0.75–6.5 µm and 0.902 in 6.5–15 µm respectively. The enhancement in short band was mainly due to the introduction of Ca²⁺ and Tb³⁺ ions that generated oxygen vacancies in the lattice forming impurity levels within the forbidden band, moreover, the transfer of Tb³⁺ to Tb⁴⁺ increased the concentration of free electrons, which promoted the absorption of free carriers. The increase in long band attributed to the lattice distortion that reduced the lattice symmetry and strengthened the absorption of lattice polar vibration.     

Wear of aligned silicon nitride under dry sliding conditions

The wear behaviour of textured silicon nitride (Si₃N₄) ceramics with aligned microstructures was analyzed under abrasive wear conditions. Dry reciprocating self-mated ball-on-flat wear tests were performed to study the influence of different microstructural plane/orientation combinations on the Si₃N₄ tribological behaviour. Textured materials showed superior wear resistance than non-textured reference Si₃N₄ for the whole range of loads and contact pressures, 5–50 N and 1.7–3.6 GPa, respectively, with an increase of about 70% for the maximum applied load. Within textured materials, the plane perpendicular to the extruding direction exhibited a 50% higher wear resistance (4 × 10^?5 mm³ N^?1 m^?1) than the parallel plane where the elongated grains were aligned. The severe wear process involved debonding, fracture and debris formation mechanisms. The progress of this sequence depended on the particular microstructure of each plane/orientation combination. A relationship between abrasive wear resistance and selected microstructural parameters has been established.     

Magnetic resonance imaging (MRI) of PEM dehydration and gas manifold flooding during continuous fuel cell operation

Magnetic resonance imaging (MRI) was employed for visualizing water inside a proton exchange membrane (PEM) fuel cell during 11.4 h of continuous operation with a constant load. Two-dimensional images acquired every 128 s revealed the formation of a dehydration front that propagated slowly over the surface of the fuel cell membrane—starting from gas inlets and progressing toward gas outlets. After traversing the entire PEM surface, channels in the gas manifold began to flood on the cathode side. To establish a qualitative understanding of these observations, acquired images were correlated to the current output and the operating characteristics of the fuel cell. Results demonstrate the power of MRI for visualizing changing water distributions during PEM fuel cell operation, and highlight its potential utility for studying the causes of cell failure and/or strategies of water management.     

Mechanical response of cracked laminated plates

Laminated glass plates are used in several safety, security and transportation applications to enhance their structural integrity. The mechanical behavior of laminates after glass-fracture, for example energy absorbing capacity and residual stiffness, determines their utility. However, the combined influence of glass fragmentation, large deformations and interfacial decohesion has been difficult to assemble in models capable of directing mechanical design. This study provides a framework for such analyses. The cracked plate is modeled as a collection of stiff glass fragments connected by elastomeric bridging ligaments. The behavior of the elastomer layer is represented by an analytical bridging model, validated and calibrated through experiments. For simple regular crack patterns, an analytical model has been developed to study the post-cracking response of laminated glass plates. This model predicts the compliant behavior of a cracked laminated plate as a function of the adhesive strength, thickness and elastic properties of the elastomer layer, and the number of fragments and the size of the plate. Based on the bridging behavior of the elastomeric ligament, an interface/bridging finite element has been formulated for numerical simulations of cracked laminates. Mechanical behavior of cracked laminates has been studied through model experiments and excellent agreement between experiments and theory is observed.     

Crossover from grain boundary sliding to rotational deformation in nanocrystalline materials

A theoretical model is suggested which describes cooperative action of grain boundary (GB) sliding and rotational deformation in mechanically loaded nanocrystalline materials. Focuses are placed on the crossover from GB sliding to rotational deformation occurring at triple junctions of GBs. In the framework of the model, gliding GB dislocations at triple junctions of GBs split into dislocations that climb along the adjacent boundaries. The splitting processes repeatedly occurring at triple junctions give rise to climb of GB dislocation walls that carry rotational deformation accompanied by crystal lattice rotation in grains of nanocrystalline materials. The role of GB sliding, rotational deformation and conventional dislocation slip in high-strain-rate superplastic flow in nanocrystalline materials is discussed.     

A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly

Human-robot collaborative (HRC) assembly has become popular in recent years. It takes full advantage of the strength, repeatability and accuracy of robots and the high-level cognition, flexibility and adaptability of humans to achieve an ergonomic working environment with better overall productivity. However, HRC assembly is still in its infancy nowadays. How to ensure the safety and efficiency of HRC assembly while reducing assembly failures caused by human errors is challenging. To address the current challenges, this paper proposes a novel human-cyber-physical assembly system (HCPaS) framework, which combines the powerful perception and control capacity of digital twin with the virtual-reality interaction capacity of augmented reality (AR) to achieve a safe and efficient HRC environment. Based on the framework, a deep learning-enabled fusion method of HCPaS is proposed from the perspective of robot-level fusion and part-level fusion. Robot-level fusion perceives the pose of robots with the combination of PointNet and iterative closest point (ICP) algorithm, where the status of robots together with their surroundings could be registered into AR environment to improve the human's cognitive ability of complex assembly environment, thus ensuring the safe HRC assembly. Part-level fusion recognizes the type and pose of parts being assembled with a parallel network that takes an extended Pixel-wise Voting Network (PVNet) as the base architecture, on which assembly sequence/process information of the part could be registered into AR environment to provide smart guidance for manual work to avoid human errors. Eventually, experimental results demonstrate the effectiveness and efficiency of the approach.     

AR-based deep learning for real-time inspection of cable brackets in aircraft

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.     

Digital twin-driven manufacturing equipment development

Currently, expectations of shorter time-to-market and improved product performance are placing greater demands on manufacturing companies. However, the optimization and redesign work between the design stage and the prototype design and manufacturing stage in the traditional product development process lengthens the required product development cycle time (which lasts up to several years in extreme cases). The manufacturing phase for the physical prototype of the product is especially time-consuming and costly. The above reasons make the common product development process increasingly unable to meet the demands of market needs. Motivated by this need, the digital twin (DT)-driven manufacturing equipment (ME) development method is studied in this paper. This method contains three main core elements of the design method based on axiomatic design (AD) theory, the construction of DT models related to ME development, and DT-based validation analysis. The advantage of this method is that it can incorporate the physical prototype manufacturing stage into the digital space with the high-fidelity model provided by the DT technology, which ensures the confidentiality of the design scheme validation while freeing it from the physical prototype stage. This avoids the cost of physical prototyping, shortens the product development cycle, and improves the efficiency of new ME development. At the end of this paper, a case study of the development of a virtual machining dynamic performance test bench (VM-TB) is carried out to show the implementation flow of this proposed method, and its operability and effectiveness are verified.     

Aramid fibers reinforced silica aerogel composites with low thermal conductivity and improved mechanical performance

Aramid fibers reinforced silica aerogel composites (AF/aerogels) for thermal insulation were prepared successfully under ambient pressure drying. The microstructure showed that the aramid fibers were inlaid in the aerogel matrix, acting as the supporting skeletons, to strengthen the aerogel matrix. FTIR revealed AF/aerogels was physical combination between aramid fibers and aerogel matrix without chemical bonds. The as prepared AF/aerogels possessed extremely low thermal conductivity of 0.0227 ± 0.0007 W m⁻¹ K⁻¹ with the fiber content ranging from 1.5% to 6.6%. Due to the softness, low density and remarkable mechanical strength of aramid fibers and the layered structure of the fiber distribution, the AF/aerogels presented nice elasticity and flexibility. TG–DSC indicated the thermal stability reaching approximately 290 °C, can meet the general usage conditions, which was mainly depended on the pure silica aerogels. From mentioned above, AF/aerogels present huge application prospects in heat preservation field, especially in piping insulation.