电机智能制造中轴料加工工艺的改进

针对批量的电机轴料加工的过程进行研究,在智能制造前提下,通过对轴加工工艺改进,设计了智能轴料加工的工艺模型。在双动力智能车床组合的基础上,由集成工控机集中协调机床组合、桁架机器人和关节机器人,搭配自动上、下料系统,组成一个智能化的自动轴料加工单元。自动加工单元具备软硬件双重保护功能,有很强的检测、监测运行功能,并自带数字通信系统,纳入智能数字化车间管理系统。     

同轴探针对称激励矩形波导模式谱图研究

文章用输入阻抗方法探讨同轴探针中心对称激励矩形波导在多模传输时所能激发的传输模式。说明采取一定的措施,可以避免产生某些高次模式。并应用分式线性变换检测法,以精心设计的实验验证了理论分析的正确性。     

某型涡轴发动机喘振攻关与验证

针对某型涡轴发动机夏季试车时常发生加速喘振故障的问题,使用故障树初步分析引起喘振的主要原因,并通过径向扩压器叶片进行流场仿真、串装、多台发动机对比计量、试车试验等,对喘振原因进行深入分析。研究表明径向扩压器进口叶根倒角变大对发动机加速性能有一定程度的改善作用,大气条件对发动机加速性有一定影响,径扩面积越大、叶根圆角与进气边转接越顺滑,越有利于加速性能。     

光学元件数控单轴加工中的边缘效应控制建模方法

边缘效应严重制约了计算机控制光学表面成型技术（CCOS）的加工精度和加工效率，是亟待解决的技术难点之一。基于Preston方程，建立工具盘在加工工件边缘时的定量去除模型，是解决该问题的重要途径。将加工工件、工具盘尺寸，磨削压力、速度、时间，磨削点位等加工参量进行数学建模，建立边缘去除函数模型，精确计算边缘压力分布和磨削累计时间。随着工具盘在工件边缘露边量增加，工具盘压力呈指数级增长。工件磨削累计时间随工具盘中心点位不同呈现分段变化规律。研发单轴机数控设备，利用工具盘沿工件边缘母线点位移动方式，进行边缘效应控制研究。通过实验精确求出Preston方程比例系数k，验证模型仿真与实际加工结果吻合性，准确度达到91.9%。数控单轴机修边方法和建立的数学模型可以很好地指导实际研磨抛光过程。     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.