An error analysis and optimization method for combined measurement with binocular vision

The accurate measurement of surfaces of large aviation components is vital for the assessment of manufacturing and assembly quality of such components. To satisfy the measurement requirement of large-size components, most current researches pay more attention to combined measurement methods utilizing different measuring instruments, but the related researches on error analysis and optimization methods are not taken enough attention. This paper proposes a combined laser-assisted measurement method with feature enhancement techniques, and it also develops an error propagation model of the main factors affecting the overall measurement error in detail. Firstly, the surface of a large-size component is measured by the measurement system at multiple stations. Secondly, a control point coordinate system is established as a bridge to unify all local measurement data into the global coordinate system. To improve the overall measurement accuracy, the pixel extraction error as a key factor causing the overall measurement error is analyzed in detail. Next, the error propagation model is established, and some optimization strategies of layout for minimizing measurement error and transformation error are researched. Finally, experiments are carried out to verify the effectiveness of the proposed method. The results show that the measurement error of the proposed method reaches 0.073% and 0.14% with a 1D standard ruler and a flat plate, respectively.     

基于寿命法的压力敏感涂料静/动态特性实验

压力敏感涂料测量技术在内外流表面压力测量方面具有独特的技术优势。涂料光谱特性及"传感器"特性的研究对于分析喷涂固化中环境差异等不可控因素的影响程度、检验拟用涂料配方特定试验的适用性具有十分重要的意义。为此，以一种在研的磷光压力敏感涂料为对象，基于自主研发的静态校准系统对发光寿命随压力与温度的变化规律进行了实验研究，通过对测量结果的拟合分析发现所用涂料的温度适用范围在30~60℃之间，且可进行温度敏感度修正；基于自主研发的高频动态压力光学校准系统对涂料在不同压力脉动频率下的涂料发光寿命峰峰值规律进行了实验研究，通过分析研究确定所用涂料基于发光寿命的截止频率上限为0.94 kHz。实验研究进一步验证了涂料特性静动态校准组合方法的可行性与有效性，为压力敏感涂料配方的研发及其应用拓展提供了重要的技术支撑。     

一种中心型间断有限元MMALE方法

针对多介质可压缩流体动力学问题,提出了一种单元中心型二维MMALE(Multi-Material Arbitrary Lagrangian-Eulerian)方法。在拉氏步,流体力学方程组采用中心型间断有限元方法求解。对于混合网格,采用Tipton压力松弛模型更新物理量,用等参坐标法更新物质中心点坐标。界面重构采用一种健壮的MOF(Moment of Fluid)方法。在重映步提出了基于多边形相交的二阶积分守恒重映方法。该方法分为四个部分:多项式重构、多边形相交、积分和后验校正。多边形相交使用"剪裁投影"算法,显著降低了多边形相交算法的复杂度。后验校正是基于MOOD (Multi-dimensional Optimal Order Detection)限制策略,并做了一些改动以适应多介质的计算。数值算例表明,该方法具有二阶的精度和较好的鲁棒性。     

基于机器学习的空间翻滚目标实时运动预测

借助监督式机器学习（ML）方法，对空间翻滚目标的运动状态预测问题进行研究，为空间机器人抓捕空间翻滚目标提供可靠的数据依据。基于物理模型的运动预测方法依赖理想的建模假设，需要连续的视觉反馈信息，解决目标预测问题的能力有限。因此，本文采用机器学习中纯数据驱动方式的稀疏伪输入高斯过程（SPGP）回归方法进行空间翻滚目标的运动预测。给定空间翻滚目标运动状态的历史观测数据，通过连续优化真实观测数据，得到稀疏的伪训练数据集，进而在线快速预测目标的运动状态，预测的计算效率达到毫秒级。此外，利用马尔科夫链蒙特卡洛（MCMC）法处理连续优化过程，克服由于随机初始值造成的优化过程陷入局部极小值问题。利用Snelson数据验证了所提稀疏伪输入高斯过程回归方法的正确性，并通过4组仿真算例验证了所提方法对于空间翻滚目标运动预测的有效性和鲁棒性。     

涡轮基组合动力与火箭的耦合特性分析及匹配优化设计

基于高斯伪谱航迹优化方法，建立了"火箭辅助型涡轮基组合动力"的飞行器/推进系统匹配分析方法，针对地面水平起降、以马赫数5巡航的高超声速飞行器，以巡航航程最远为目标，完成了涡轮基组合动力（TBCC）与火箭的耦合特性分析及匹配优化设计。研究结果表明：对于可行的TBCC方案（起飞推重比为1.0），引入合适推力的火箭有助于提升加速爬升段的总效率并降低质量消耗，且对巡航航程有着一定的提升（4%起飞重量推力火箭可增加航程0.97%）；对于不可行的TBCC方案（起飞推重比为0.8），引入火箭不仅可实现推进系统方案的收敛，且其巡航航程相比可行的TBCC方案最多可增加7.9%。考虑到TBCC较大的"死重"和较低的单位迎面推力对巡航性能的不利影响，结构质量占比为25%的巡航型飞行器建议采用"13%起飞重量推力火箭辅助起飞推重比为0.7的TBCC "推进系统。相比之下，结构质量占比为55%的加速型飞行器建议采用" 5%起飞重量推力火箭辅助起飞推重比为0.98的TBCC"推进系统。     

A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper, a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed. Firstly, the invariable parameters in the forming process including shot size, mass flow, peening distance and peening angle are determined according to the empirical and machine type. Then, the optimal value of air pressure for the whole shot peening is selected by the experimental data. Finally, the feeding speed for every shot peening path is predicted by regression equation. The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object, and four experiments are conducted. In order to design specimens for acquiring the forming data, one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens, which were peened along the middle of the two stringers. The most striking finding of this experiment is that the outer shape error range is below 3.9%, so the plate specimens can be used in predicting feeding speed of the integral panel. The second experiment is performed and results show that when the coverage reaches the limit of 80%, the minimum feeding speed is 50 mm/s. By this feeding speed, the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object, and the optimal air pressure is 0.15 MPa. Then, the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment, and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method. The algorithm is validated by forming a test part and the average deviation is 0.496 mm. It is shown that the approach can realize the forming of the integral panel precisely.     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

基于蒙特卡罗法的航空发动机空气系统稳态算法优化

针对目前航空发动机空气系统稳态算法中收敛性依赖初值的问题，将蒙特卡罗方法与流体网络法综合应用到空气系统可压缩流体一维网络计算中，提出了一种新的计算方法Monte Carlo-Fluid Network（MC-FN）。该方法将空气系统简化为由节点和元件组成的网络，借助蒙特卡罗方法获得空气系统内各节点压力分配，再根据空气系统中各元件流阻特性和换热特性计算流量、温度。计算中通过将游动次数比较少的蒙特卡罗方法的计算结果作为流量残差法节点压力、温度的初始值，实现快速求得精确收敛解。与流量残差算法相比，MC-FN方法计算精度不变，收敛速度提升了66.5%；与线性求解法相比，MC-FN方法的计算精度提升了25.2%，收敛速度提升了43.8%。     

微小刀具同轴全息对刀方法研究

针对现有对刀方法存在的各种不足,提出了一种微小刀具的同轴全息对刀方法。在对全息成像原理和全息图像关键问题进行研究与分析的基础上,搭建了一套微细铣刀的全息对刀检测装置,并利用标准刀具在五轴加工中心进行了微细铣刀的对刀测量试验。试验结果表明:该对刀装置能够高效地进行微细铣刀的对刀检测,系统相对检测精度达5.1μm。在相同检测条件下,与高精度机外预调仪的测量结果相比,接触式对刀仪的相对误差为0.033%,而全息对刀样机的相对误差为0.007%,后者更能有效地实现微小刀具的对刀检测。该结果验证了微小刀具的同轴全息对刀方法的可行性,即全息测量可用于微细铣刀的高精度对刀。     

基于矩化GO法的某型电动飞机扰流板系统可靠性

扰流板冗余系统提升了新能源电动飞机的可靠性。采用GO法建立电动飞机扰流板系统可靠性模型，设计SIMULINK仿真界面，针对操作符11给出一种新的运算模块。针对扰流板冗余系统设计一种矩化操作符，给出运算流程，有效减少传统GO法在冗余系统建模工作量，利用故障树分析法对结果进行验证。结果表明，本文提出的方法切实可行，为新能源电动飞机扰流板系统可靠性分析提供了新的思路。