外文翻譯---鉆床的刀具失效無傳感器監(jiān)測系統(tǒng)

1、東北大學(xué)畢業(yè)設(shè)計(jì)（論文） 附錄 A附錄 A 外文翻譯原文Sensorless tool failure monitoring system for drilling machinesLuis Alfonso Franco-Gascaa, Gilberto Herrera-Ruiza, Roc?´o Peniche-V

2、eraa, Rene´ de Jesu´s Romero-Troncosob, Wbaldo Leal-TafollacAbstractIt is well known that on-line tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible

3、 damages to the workpiece or the machine tool, reliable techniques are required providing an on-line response to an unexpected tool failure. Drilling is one of the most fundamental machining operations and two of the mos

4、t crucial issues related to it are tool wear and fracture. During the spindle process, the motor driver current is related to the drill condition: power consumption is higher for a worn drill in comparison to a sharp dri

5、ll for the same process. This difference in power consumption can be self-correlated to obtain the resulting waveform variance to provide a merit figure for tool condition. This paper describes a driver current signal an

6、alysis to estimate the tool condition by using the discrete Wavelet Transform in order to extract the information from the original cutting force, and through an autocorrelation algorithm evaluate the tool wear in the fo

7、rm of an asymmetry weighting function. The current is monitored from the motor driver to give a sensorless approach. Experimental results are presented to show the algorithm performance, a complete sensorless tool failur

8、e system which allows the detection of tool failure as a function of spindle current in real time.Keywords: Tool failure; Wavelet transform; Tool monitoring1. IntroductionCutting tools represent the highest cost in the p

9、roduction process of the manufacturing 東北大學(xué)畢業(yè)設(shè)計(jì)（論文） 附錄 Apreserved, while spurious data are minimized. The designed filter does not eliminate all spurious components

10、, but a subsequent Discrete Wavelet Transform (DWT) will enhance the cutting force signal by its filter bank property. The wavelet transform brings a time-frequency representation of a signal in decimated form depending

11、on the application detail level; the result will be given as time domain samples at the decimated frequency in a compressed form [12]. The bases of the Wavelet Transform are the wavelets, generated from a basic wavelet f

12、unction by dilations and translations. Given a time-varying signal f(t), a wavelet transformation consists of computing coefficients that are the inner products of the signal and a family of wavelets [9]. By DWT we under

13、stand the continuous wavelets with the discrete scale and translation factors [10].The DWT is defined as stated in Eq. (1), where cj,k is called the wavelet coefficient. This may be considered as a time-frequency map fro

14、m the original signal f(t). An approach of multi-resolution analysis is used on the discrete scale function, defined together with Eq. (3).(1) dt t f c k j k j? ? ? , , ) ( ?(2) ) 22 ( 2 2, jj jk jk t c ? ?? ?(3) dt t t

15、f d k j k j ?? ? ) ( ) ( , , ?where dj,k is called the scale coefficient and is the sampled version of the original signal. The DWT computes the wavelet coefficients cj,k and dj,k (jZ1,., J) given by Eqs.Fig A.1 Additive