粒子测速关键技术研究

发布时间：2018-04-30 07:09

本文选题：真空冷喷涂粒子 + 测速　；参考：《西安工程大学》2015年硕士论文

【摘要】：基于空气动力学原理,真空冷喷涂是在室温及真空条件下,通过高压混合气体加速超细固态陶瓷粉末颗粒以一定的速度撞击基体,从而实现涂层制备。真空冷喷涂技术的特点是沉积效率高、沉积温度低,涂层结构致密,陶瓷颗粒不易产生氧化、相变、组织变化等问题,因而真空冷喷涂研究受到了广泛关注。粒子飞行速度大小是真空冷喷涂技术中至关重要的性能参数之一,直接影响涂层制备效果和质量,粒子飞行速度的研究具有重要意义。国内某大学真空冷喷涂实验室研究人员,在进行真空冷喷涂粒子飞行速度的测算研究中,通过自主研发的粒子参数采集系统平台,采集粒子飞行时产生的电压数据,再通过示波器软件存储电压数据和通用分析软件来测算粒子的飞行速度。但其粒子测速实验方案效率不高,无法将粒子数据的采集、显示和分析一体化执行。本文介绍如何在真空冷喷涂粒子测速研究基础上,研发集真空冷喷涂粒子参数实时采集、显示和分析为一体的真空冷喷涂粒子测速软件,并且介绍了软件研发中的相关技术和实施方案。测速软件解决了真空冷喷涂粒子在数据实时采集存储、显示和计算分析上一体化执行的问题,提高真空冷喷涂粒子测速实验效率,满足了真空冷喷涂技术研究的要求。本文主要任务如下:(1)设计真空冷喷涂粒子飞行速度检测软件,软件通过内存映射文件方式实时存储粒子电压信号,并通过Tee Chart控件将粒子电压信号清晰显示在测速软件中。(2)对粒子信号进行时域和频域分析,对比多邻点平均滤波法、快速傅里叶变换(fast fourier transform,简称FFT)带通滤波和FFT低通滤波法处理后的粒子信号的性能参数和特点,确定在测速软件中使用FFT[0-1.22MHz]低通滤波零漂补偿加16邻点平均滤波的组合方法滤除信号中的噪声。(3)对粒子噪声信号进行FFT[0-1.22MHz]低通滤波零漂补偿加16邻点平均滤波处理,根据噪声信号高斯分布特性确定有效粒子脉冲信号幅值大小,并通过矩形窗函数截取有效粒子脉冲信号。(4)对有效粒子脉冲信号数据进行最小二乘法线性拟合,计算出有效粒子脉冲信号拟合方程系数,并通过系数得出最新粒子上升沿和下降沿拟合数据。(5)根据最新的拟合数据,通过测速算法得出每屏粒子飞行速度值,以速度平均值为最终实验结果并显示在测速软件中。
[Abstract]:Based on the principle of aerodynamics, vacuum cold spraying is used to accelerate the impact of superfine solid ceramic powder particles on the substrate at a certain speed through high pressure gas mixing under room temperature and vacuum conditions, so that the coating can be prepared. The vacuum cold spraying technology is characterized by high deposition efficiency, low deposition temperature, dense coating structure, difficult oxidation of ceramic particles, phase transformation, microstructure change and so on, so the research of vacuum cold spraying has been paid more and more attention. Particle flying velocity is one of the most important performance parameters in vacuum cold spraying technology, which directly affects the preparation effect and quality of coating, and the study of particle flying velocity is of great significance. In the course of measuring and studying the flying velocity of vacuum cold spraying particles, researchers in a vacuum cold spraying laboratory at a university in China collect the voltage data generated by particles flying through a self-developed particle parameter acquisition system platform. Then the velocity of particles is calculated by storing voltage data and general analysis software with oscilloscope software. However, the experimental scheme of particle velocimetry is not efficient, so it can not carry out the collection, display and analysis of particle data. This paper introduces how to develop the software of vacuum cold spray particle velocity measurement based on the research of vacuum cold spray particle velocity measurement, which can collect, display and analyze the parameters of vacuum cold spray particle in real time. And introduced the software research and development related technology and the implementation plan. The software solves the problem of real-time data collection and storage, display and calculation and analysis of vacuum cold spray particles, and improves the efficiency of the velocity measurement of vacuum cold spraying particles, and meets the requirements of the research of vacuum cold spraying technology. The main tasks of this paper are as follows: (1) designing a software for detecting the flying velocity of cold sprayed particles in vacuum. The software stores the voltage signals of particles in real time by memory mapping file. The particle voltage signal is clearly displayed in the velocimetry software by the Tee Chart control. The particle signal is analyzed in time domain and frequency domain, and the multi-neighbor point average filtering method is compared. The performance parameters and characteristics of particle signals processed by fast fourier transform bandpass filtering and FFT low-pass filtering. It is determined that FFT [0-1.22MHz] low-pass filter zero-drift compensation plus 16-neighbor average filtering method is used to filter the noise in the signal in the velocity measurement software. The particle noise signal is filtered by FFT [0-1.22MHz] low-pass filter zero-drift compensation plus 16-neighbor average filter. According to the distribution characteristics of noise signal Gao Si, the amplitude of effective particle pulse signal is determined, and the effective particle pulse signal is intercepted by rectangular window function. The fitting equation coefficient of effective particle pulse signal is calculated, and the fitting data of rising edge and descending edge of the latest particle are obtained by the coefficient. (5) according to the latest fitting data, the velocity value of particles per screen is obtained by the velocity measurement algorithm. The average velocity is taken as the final experimental result and displayed in the software of velocity measurement.
【学位授予单位】：西安工程大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TQ639;TQ174.7

【参考文献】