A trajectory planning method on error compensation of residual height for aero-engine blades of robotic belt grinding

While the traditional trajectory planning methods are used in robotic belt grinding of blades with an uneven machining allowance distribution, it is hard to obtain the preferable profile accuracy and surface quality to meet the high-performance requirements of aero-engine. To solve this problem, a novel trajectory planning method is proposed in this paper by considering the developed interpolation algorithm and the machining allowance threshold. The residual height error obtained from grinding e...     

极区横向地理系空间稳定型惯导算法研究

针对地心惯性系和当地地理系的空间稳定型惯导系统导航算法在极区导航失效的问题,提出了适用于空间稳定型惯导系统的极区导航算法。该算法通过伪经纬网构建了横坐标系参考框架,建立了横向地理系空间稳定型惯导系统力学编排,并在此基础上重新推导了通用的误差模型。最后,通过极点附近区域与穿越极点区域仿真分析了算法的极区有效性。仿真结果表明该算法在极点附近区域解算的伪航向角误差小于3′,伪经度误差小于4′;在穿越极点区域解算的伪纵摇角、伪横摇角误差小于0.3′,伪航向角误差小于3′,伪东向、伪北向速度误差小于1m/s,伪经度、伪纬度误差小于4.2′。该算法克服了极区导航计算溢出、误差放大等问题,提高了系统的极区导航精度,能够满足极区导航要求。     

An alternative method to reduce process-induced deformation of CFRP by introducing prestresses

Process-induced deformation is an important limiting factor to the application of high performance composite structures. An alternative method to reduce process-induced deformation is proposed by introducing prestress to a single layer or part of the layers instead of all the applicable layers. In this method, the necessary prestress level that applied to the single layer was in the reasonable range. Two kinds of unsymmetric laminates are manufactured at varying prestress levels. The experimental results described that the curing deformation is changed rapidly with the growing prestress level and flipped after a certain prestress level. Moreover, there is a good linear relationship between prestress level and final curing deformation, which gives a convenient way to calculate the prestress level that can fully counteract the curing deformation. The numerical model to predict the curing deformation was built. A good agreement between the numerical and the experimental results shows the effectiveness of the numerical model. To predict the prestress level that can fully counteract the curing deformation, an analytical model is also proposed. Theoretically, the prestress method can fully counteract the curing deformation of the specimens, while the experimental results show that the prestressing layer reduces more than 80% of the curing deformation.     

残余应力超声测量技术影响因素研究

针对大型空间站寿命和质量进一步提高带来的残余应力精确测量问题，开展了影响残余应力测量准确性的因素和校正方法研究，在对温度、工件表面粗糙度、换能器耦合状态等原因分析的基础上，对主要影响因素引起的测量误差进行试验测定。试验结果表明：温度变化、工件表面粗糙度、换能器耦合状态等因素都可能引起较大的测量偏差，需引入温度补偿系数、表面粗糙度试块校正、优选耦合剂等修正与控制方法；有效控制影响因素，能够提高超声波检测残余应力的精准度，减小测量误差。该研究可为超声波残余应力测试技术在航天器舱体结构的应用发展提供技术参考。     

Influence and experiment of cable-net manufacturing errors on surface accuracy of mesh reflector antennas

Cable-net structures are of substantial importance in the construction of large mesh reflector antennas. Owing to the inevitable errors in their manufacturing process, the reflector surface accuracy deteriorates. This study makes a comprehensive investigation of random manufacturing errors during constructing the mesh reflector antennas, and analyze its influence on reflector surface accuracy. Firstly, the sensitivity of reflector surface accuracy with respect to the random errors of the unstressed cable length is mathematically deducted. Secondly, a non-button connecting method is proposed and analyzed to reduce manufacturing errors. Thirdly, two physical experiment models based on 2.62-meter mesh reflector antenna are made. Finally, numerical examples and experimental tests are given to demonstrate the effectiveness of the proposed method. Compared with the traditional method, the proposed method can effectively reduce the influence of the manufacturing errors on the reflector surface accuracy. Moreover, the reduction in the sizes of the nodes also reduces the risk of entanglement of the mesh reflector antenna during the deployment process, and thereby improves the deployment reliability.     

Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.     

Review on residual stress and its effects on manufacturing of aluminium alloy structural panels with typical multi-processes

In the aerospace industry, integrated aluminium alloy plates and stiffened panels with high accuracy and performance attract significant interest. To manufacture these panels as integrity with high accuracy, multiple processes need to be utilised, such as machining, welding and forming. During the whole manufacturing chain, residual stresses can be generated and redistributed in the components among different processes. The residual stress would significantly affect the shapes and properties of the final products. Currently, these great effects are not well considered in the design and manufacturing processes. This paper aims to draw a general understanding of the residual stress generated in the pre-manufacturing processes and its effects on subsequent manufacturing processes. The mechanisms and distributions of residual stresses generated in typical pre-manufacturing processes of structural panels, including machining, welding and additive manufacturing (AM), are firstly summarised. The detailed effects of generated residual stresses on distortion and application properties in subsequent manufacturing processes are then concluded. In addition, current methods developed for the investigation of residual stress effect in multi-processes manufacturing are critically reviewed, including experimental, analytical, finite element (FE) and machine learning methods. Furthermore, the future development trend of methods for residual stress consideration and control in the design of manufacturing processes is summarised, providing comprehensive guidance to achieve the high accurate manufacturing of aluminium alloy structural components.