加工信号

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  • processing signal
加工信号加工信号
  1. 常规放电状态检测方法由于仅采用单一的特征量进行状态判别,因此在加工信号频率高、加工波形产生畸变的微细电火花加工中,不再适用。

    Conventional discharge condition detection methods extracting only certain eigenvalue are not suitable for the Micro-EDM due to the high frequency and distortion of voltage wave shape .

  2. 本文基于结构实验模态分析技术,对磨床空转及磨削工况的噪声及部分测点的加速度信号进行了拾取分析,初步掌握了磨床工况下加工信号频率范围及峰值随频率分布情况。

    Based on the technology of structure 's Experimental Modal Analysis , noise and vibration signal of grinding machine ( Type MSY7115 ) under working condition were picked up and analyzed in this thesis .

  3. 从CPLD具有完全硬件逻辑的特点出发,提出了用CPLD发生占空比可调的超高频电火花加工脉冲信号的设想。

    This paper presents that developing ultrahigh frequency pulse EDM generator of adjustable duty ratio by CPLD based on the characteristic of CPLD , complete hardware logistic .

  4. 粗糙的砍没有修整表面。信号波形加工,信号修整

    Hew roughly , without finishing the surface . signal conditioning

  5. 基于切削加工声音信号的刀具状态监测技术基础研究

    Research on Monitoring Technique for Tool Wear Based on Cutting Sound Signal

  6. 主要研究内容包括三方面:(1)对经验模态分解方法的理论进行了研究,并将其应用到加工误差信号处理当中。

    The main research content includes three aspects : ( 1 ) The EMD theory is researched and applied to the machining error signal processing .

  7. 你知道,脑波的活动从枕叶&后脑勺加工视觉信号的地方,扩散到额叶。

    You see brainwave activity spread from the sensory processing area of the occipital lobe , the bit at the back of the brain that processes visual signals , to the brain 's frontal lobe .

  8. 本文首先阐述了卡尔曼滤波的基本原理,导出了卡尔曼滤波的递推表达式,然后以高精度丝杠车床为例建立了加工中误差信号的数学模型,并用PB-700型个人计算机仿真。

    In this paper , first of all the fundamental principle of Kalman filter is described and a recurrence formula for Kalman filter by a state vector is derived . Then a mathematical model of machining error signals is developed taking a high precision leading screw lathe as an example .

  9. 机床加工误差补偿信号的实时测量系统

    Real-Time Measurement System for Machining Error Compensation Control Signal

  10. 但如何获取机床加工过程声音信号的特征以进行机床类型、状态及其加工参数的识别是啻待解决的问题。

    But how to get the characteristics for recognition is urgent to be resolved .

  11. 木材铣削加工声发射信号的特征提取与模式识别的研究

    The Research of Characteristics of Acoustic Emission Signal Extraction and Pattern Recognition in Timber Milling

  12. 实验结果表明,自由曲面加工的故障信号能很好被检测区别开来。

    Experimental results show that the fault signal in free-form surfaces machining can be clearly monitored and distinguished .

  13. 马舒尔等称其为“回返加工”:信号从感觉区进入加工区,然后再返回感觉区。

    Mashour and others call this " recurrent processing " : Signals travel from the sensory areas to the processing areas and back again .

  14. 建立了铣削加工中振信号的检测系统,并介绍了利用振动信号进行铣刀破损试验的整个试验过程。

    The detecting system of vibration signal is established dur in g milling process and introduced the whole experiment process of using vibration signal in tool breakage .

  15. 反射式金属光栅光栅盘基材与光栅盘加工及光栅信号的提取,与传统玻璃光栅盘有很大区别。

    Material used for the disk and method for manufacturing the metal grating disk as well as the extraction of grating signal are quite different from the traditional glass grating disk .

  16. 同时,我们将正常加工状态的信号与严重刀具磨损需要更换刀具时的信号进行比较,通过小波分析我们可以确定发生严重磨损的时间点。

    At the same time , we will contrast the normal signal and the severe cutter wearing signal in that we have to replace the cutter . Trough the wavelet analysis , we can make sure the time of severe cutter wearing state .

  17. 加工状态下实测信号的AR最优建模

    Optimum AR Modeling of Signal Under Working Condition

  18. 磨削加工的声发射信号分析

    Analysis of Acoustic Emission Signal in Grinding Signal Processing Acoustic Ecology

  19. 基于信誉建设的加工农产品品牌信号传递效应分析

    Analysis of Brand Signaling Effects of Agricultural Products Based on Credit Building

  20. 磨削加工中声发射信号特性分析及其在烧伤预报中应用研究

    Analysis on the Characteristics of AE Signals during Grinding and its Application in the Prediction of Grinding Burn

  21. 针对异型螺杆铣削加工,采用振动信号特征值作为检测刀具磨损的参量。

    The characteristic values of vibration signals were used as testing parameters of tool wear for milling special shaped spiral rods .

  22. 使用单片机进行智能防火监控,存在着太阳光和加工火花等干扰信号的影响。

    Sun 's rays and sparkle produced during manufacturing have apparent impact on the correctness of detecting fire if using SingleChip as control component .

  23. 加工过程中振动信号在某个频带内幅值的变化,能够充分表征刀具的当前情况。采用小波变换技术构造滤波器组并提取刀具磨、破损特征信号。

    So group filtering via wavelet transform is constructed first . And tool wear & breakage character signals are extracted based on which vibration amplitude change and tool state is mapped .

  24. 这两种方法完全消除了加工参数变化对信号特征的影响,提高了监测系统的识别精度,能够构建任意加工条件下的刀具磨损监测系统。

    The two methods completely eliminate machining parameter effect on features , and improve the recognition precision of monitoring system , and also , it can build the tool wear monitoring system under arbitrary machining conditions . 3 .

  25. 该理论认为情绪信息加工不是单纯的信号加工过程,身体起到很重要的作用,自身的身体状态、活动方式等都为情绪的加工和理解奠定基础。

    The embodied emotion theory thought that the processing of emotional messages was not merely signal processing , but the body played a very important role . In more details , body state and the way of activity laid the foundation for the processing and understanding of emotion messages .

  26. 在微细电火花加工中,由于加工信号的频率高、加工波形的畸变,使得常规放电状态检测法已不再适用。

    The discharge condition detection method sused in conventional EDM ( Electirc Discharge Manufacturing ) are not suitable for the Micro EDM due to the high frequency and distortion of voltage wave shape .

  27. 本文提取出了加工过程中刀具的各种状态信号特征值并进行状态辨识,通过对加工过程的状态信号的对比,分析出适合作为刀具磨损监测的信号。

    The paper consists of picking-up all kinds of signal eigenvalues in machining process , doing some fettle discrimination and analyzing all kinds of elements which may influence tool wearing in milling the workpiece .

  28. 把噪声作为信号,具有获取容易,对加工过程没有任何影响的优点,是监测加工过程的理想信号。

    These machining noises can be regarded as ideal signal to monitor machining because it is easy to get without any influence on machining process .

  29. 本论文重点研究以下几个方面:首先,通过对超精密加工机床产生的振动进行全面的分析与研究,最终得出超精密加工机床振动信号的分析和奇异性监测必须通过小波变换来分析。

    This paper focuses on the following aspects : First of all , through the ultra-precision machine tool vibration to conduct a comprehensive analysis and research , concluded that ultra-precision machine tool vibration signal analysis and singularity of monitoring must be analyzed through the wavelet transform .