激光熔化沉积快速成形TC4钛合金的焊接性能

为了探索激光熔化沉积快速成形技术制备TC4钛合金的焊接性能,分别采用电子束焊、激光焊两种方法制备接头试样,并借助金相、硬度试验等方法获得接头力学性能、显微组织及硬度。结果表明:两种焊接方法得到的接头抗拉强度最高达953 MPa,焊接系数均0.9;激光和电子束焊接焊缝为网篮状α'相马氏体组织,热影响区为α相和针状马氏体组织组成。激光熔化沉积快速成形TC4钛合金和传统工艺制造的TC4钛合金在焊接特性方面表现相当。     

GH3625合金激光直接成形工艺

为了研究工艺参数对金属激光直接成形零件质量的影响,通过正交试验的方法对单道单层GH3625合金激光直接成形工艺进行了研究,主要探究了激光功率、扫描速度、送粉速度等参数对单道单层成形截面宽度和高度的影响规律,提出预测模型并做了验证。结果表明,在单道单层成形中,激光功率、送粉速度、扫描速度等工艺参数对单道宽度和高度具有不同程度的影响,其中,对于单道成形宽度和高度来讲,扫描速度对其影响最为明显。试验验证结果表明借助Minitab软件得到的线性回归模型是有效的。     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.     

基于惯性/激光测距/视觉里程计组合的高空场景尺度误差估计方法

惯性/视觉感知信息融合导航定位技术是目前实现无人机不依赖卫星自主导航的最有效手段。但对于面向高空场景的大型无人机，惯性器件误差与视觉里程计尺度误差耦合且特征平面化导致可观测性下降。针对这一问题，提出了利用惯性/激光测距/视觉里程计组合实现尺度误差估计的方法。通过开展误差模型建立、激光测量点与图像中位置匹配、无人机平飞机动下系统可观测性分析等关键技术研究，实现了高空场景下尺度误差的精确估计。经过300m高度机载试验数据验证，算法精度优于1.5%D，对卫星拒止条件下高空无人机自主导航具有重要意义。