基于落角约束的最优导引律

在对地精确制导武器的实际使用中,不仅要求脱靶量最小,而且希望能以一定的落角命中目标,从而可以充分发挥战斗部效能,取得最佳毁伤效果。传统的比例导引律不能满足该要求,为此本文建立了含有落角约束的最优控制模型,并依据施瓦兹不等式原理进行了推导,最终得到了含有落角约束的最优导引律的表达形式。仿真结果表明:该导引律可以使导弹以期望的落角命中目标,且具有实现简单、结果最优等特点。     

Influence of thickness and initial groove angle in M–K model on limit strain of 7B04 by considering through-thickness stress

Marciniack–Kuczinski (M–K) model is widely used to predict material’s forming limit curve (FLC). The prediction of FLC traditionally neglected through-thickness normal stress. However, it cannot be neglected in some forming processes. Much work has been done to study the effect of through-thickness normal stress on FLC with constant through-thickness normal stress or constant ratio of through-thickness normal stress and maximum principal stress. In addition, based on Nakazima test process, the ratio of through-thickness normal stress and maximum principal stress has been derived, which was a function of instantaneous thickness and loading path. Here, initial groove angle in M–K model was not considered. In this paper, uniaxial tension tests and Nakazima tests were performed on 7B04 aluminum alloy. Based on Hill 48 yield criterion and M–K model, the prediction model of FLC was established. The increase of thickness can enhance FLC. Meanwhile, it is necessary to consider through-thickness normal stress and initial groove angle in prediction model. On the left side of FLC, the effect of initial groove angle on FLC is weakened by increasing sheet thickness. On the right side of FLC, the effect of initial groove angle on FLC is strengthened by increasing sheet thickness. On the right side of FLC, the relation between limit strain points with different thicknesses is linear under one certain loading path. Thickness has decisive effect on through-thickness normal stress level and the changing trendy of through-thickness normal stress during calculation is different under different stress condition.     

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Experimental and numerical investigation on surface quality for two-point incremental sheet forming with interpolator

The unsatisfied surface quality seriously impedes the wide application of incremental sheet forming (ISF) in industrial field. As a novel approach, the interpolator method is a promising strategy to enhance the surface quality in ISF. However, the mechanism for the improvement of surface quality and the influence of interpolator properties on surface roughness are not well understood. In this paper, the influences of process variables (i.e. tool diameter, step size and thickness of interpolators) on the forming process (e.g. surface roughness, forming force and geometric error) are investigated through a systematic experimental approach of central composite design (CCD) in two-point incremental sheet forming (TPIF). It is obtained that the increase in thickness of interpolators decreases the surface roughness in direction vertical to the tool path while increases the surface roughness in direction horizontal to the tool path. Nevertheless, the combined influence between thickness of interpolators and process parameters (tool diameter and step size) is limited. Meanwhile, the placement of interpolator has little influence on the effective forming force of blank. In addition, the geometric error enlarges with the increase of step size and thickness of interpolator while decreases firstly and then increase with an increase in tool diameter. Finally, the influencing mechanism of the interpolator method on surface quality can be attributed to the decrease of the contact pressure due to the increase of contact area with the unchanged contact force. Meanwhile, the interpolator method eliminates the sliding friction on the surface of blank due to the stable relative position between the blank and the interpolator.     

基于有限Fourier级数的航天器转移轨迹设计

针对航天器小推力转移轨迹的初始设计问题，利用基于三阶Fourier级数的设计方法实现了航天器小推力的多圈转移。同时，基于有限Fourier级数的形状法，对具有多个约束条件的小推力多圈转移轨迹进行了优化设计。选取了共面同轴同偏心率的初始和末端轨道位置，对所提出的方法进行了仿真验证。结果表明：与改进逆五阶多项式形状法相比，所提出的方法虽然增加了转移时间，但当转移圈数为5圈、有限Fourier级数的项数为10时，可减少将近75%的转移速度增量，同时大大减小了所需的最大推力加速度的值。     

Feasibility evaluation of failure models for predicting forming limit of metal foils

Forming limit of metal foil is an important index to evaluate its formability, and is of considerable significance to improve the quality of products. The ductile fracture (DF) behavior in microscale plastic deformation is remarkably affected by the geometry and grain size. To explore the size-dependent forming limit curve (FLC), the Holmberg and Marciniak tests of SUS304 foils with the thicknesses of less than 0.1 mm and diverse grain sizes were performed. In addition, the validity and feasibility of three types of existing failure models including Swift/Hill, Marciniak-Kuczynski (M-K) and DF criteria for predicting the micro-scaled FLCs were discussed. It is found that the Swift/Hill model possesses the worst accuracy with predicting deviation above 50%. Four classical DF criteria including Freudenthal, Ayada, Brozzo and Oh show great difference, and Oh model considering plastic anisotropy presents the best precision. The predicted deviation of M-K model is aggravated with increasing grain size and decreasing foil thickness, which is attributed to the intensified free surface roughening and transformation of fracture mechanism with miniaturization. This research thus provides a deeper understanding and valuable reference for the widespread application of FLC in microforming.     

飞机电动滑行系统驱动特性及节能减排性能分析

针对某中程飞机设计了基于主起落架机轮驱动的飞机电动滑行系统（AETS），并对其进行了仿真分析。利用MATLAB/Simulink对AETS进行了建模，分别构建了驱动电机模型、机械系统模型和排放性能评估模型；在此基础上进行了驱动能力、稳定性及节能减排性能的仿真。结果表明:所设计系统驱动能力良好，稳定性优异，节能减排效果明显。利用所设计系统驱动目标飞机在地面上滑行最大速度可达4.91 m/s，基本满足滑行要求；当外在负载波动时，其滑行速度能够平稳调节；在相同条件下，当滑行距离为1 500 m时，利用AETS代替主发动机驱动飞机滑行可节省燃油75%以上，减排CO、HC、NO_X等有害气体67%以上。      

基于M-K模型的TA15钛合金高温成形极限

为了探究TA15钛合金高温环境下的成形极限，明确本构方程中参数对成形极限的影响规律，建立了考虑高温软化效应TA15钛合金高温环境下的本构关系，利用高温成形极限试验平台及M-K失稳理论对TA15钛合金板高温环境下的成形极限分别进行了试验测试及理论预测。理论预测结果表明当温度从800℃提升至880℃时，平面应变状态下的极限主应变由0.18提升至0.33。基于M-K失稳理论和建立的高温本构模型，分析了本构方程中的参数对成形极限的影响规律，结果表明提高加工硬化指数、速率敏感因子及减小软化因子，均可以提升应变强化率的大小，进一步延缓沟槽内应变状态趋于平面应变状态，从而提升理论成形极限曲线在应变空间中的位置。此外，理论计算结果表明速率敏感因子对成形极限曲线的左侧影响程度要大于其对右侧部分的影响，该现象主要归因于速率敏感因子对不同应变大小下的应变强化率的影响不同。      

一种圆柱物料装填机器人残余振动抑制方法

为提高载具内的物料装填效率，提出了一种在载具内受限空间中使用的新型圆柱物料装填机器人，并通过优化关节轨迹，对机器人末端残余振动进行抑制。首先，给出物料装填机器人的总体设计方案和工作流程。然后，结合物料装填机器人的结构特点，应用拉格朗日方法建立封闭形式的刚柔耦合动力学模型，并应用模态分析方法得到机器人末端动态响应计算方法。最后，以物料装填机器人关节残余弹性势能最小为优化目标，使用最大最小蚂蚁系统，对机器人关节轨迹进行优化，并对优化结果进行仿真验证。仿真结果表明，优化后的关节轨迹，在满足快速装填要求的基础上，可以降低约34.4%的关节残余弹性势能和约37.6%的机器人末端振动振幅。      

基于权重的层次矢量量化体压缩算法及其在空间环境中的应用

空间环境数据可视化是空间环境预报和服务的重要手段.目前基于矢量量化的压缩体绘制算法均只考虑了数据特点和压缩效果，并未结合具体应用需求.为适应空间环境体数据可视化具体应用需求，提出了一种应用驱动的压缩体绘制算法——基于权重的层次矢量量化算法（WHVQ）.算法将体数据划分为43的数据块，并为数据块设置权重，重点关注区域的数据块赋予相对较大的权重值.然后，对各数据块用三层结构表示.最后，对次高层和最高层分别采用基于权重的主成分分析分裂法产生初始码书，并采用基于权重的LBG算法进行码书优化和量化.实验结果表明，该算法能够在保证整体压缩效果的同时，提升局部重点关注区域的重构质量.