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Przetwarzanie sygnałów w diagnostyce stanu narzędzia i procesu skrawania

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Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono współczesne metody przetwarzania sygnałów w układach diagnostyki stanu narzędzia i procesu skrawania (DNiPS). Omówiono przetwarzanie wstępne (np. filtrowanie, wzmacnianie, zamianę na postać cyfrową, segmentację), a następnie wyznaczenie miar sygnałów w dziedzinie czasu, częstotliwości (transformata Fouriera) lub czasu i częstotliwości (krótkookresowa transformata Fouriera, transformata falkowa, transformata Hilberta-Huanga). Uzyskanych miar sygnałów może być bardzo wiele, zwłaszcza jeśli pochodzą od różnych sygnałów (czujników), a wiele z nich nie jest związanych z monitorowanym procesem. Stąd konieczne są odpowiednie sposoby oceny stopnia ich powiązania z monitorowanym zjawiskiem. Omówiono metody oceny przydatności i selekcji miar oraz eliminacji tych miar, które dublują informacje z miar lepszych od siebie.
EN
Paper presents state of the contemporary methods of signal processing in tool and process condition monitoring systems (T/PCM). First the signal pre-processing (filtering, amplification, A/D conversion, segmentation) was described. Then there have been discussed the extraction of signal features in time domain, frequency domain (Fourier transform) or time-frequency domain (Short Time Fourier Transform, Wavelet Transform, Hilbert-Huang Transform). There are many diverse descriptors especially from different sensor signals, however many of them are not correlated with monitored process. Thus, feature relevancy evaluation is of critical importance. Methods of signal feature usability evaluation, selection and elimination of these which are doubling information contained in better ones were also presented.
Rocznik
Strony
37--49
Opis fizyczny
Bibliogr. 34 poz.
Twórcy
  • Instytut Technik Wytwarzania Politechniki Warszawskiej
Bibliografia
  • [1] Al-Habaibeh A., Gindy N., A New Approach for Systematic Design of Condition Monitoring Systems for Milling Processes, Journal of Materials Processing Technology, 2000, vol. 107, s. 243-251.
  • [2] Altintas Y., Park S.S., Dynamic Compensation of Spindle-integrated Force Sensors, CIRP Annals, 2004, vol. 53, no. 1, s. 305-308.
  • [3] Binsaeid S., Asfoura S., Chob S., Onarc A., Machine Ensemble Approach for Simultaneous Detection of Transient and Gradual Anomalies in Milling Using Multisensor Fusion, Journal of Materials Processing Technology, 2009, vol. 209, s. 4728-4738.
  • [4] Christopher A., Piazza J., Roth J., Directionally Independent Failure Prediction of Endmilling Tools During Pocketing Maneuvers, Journal of Manufacturing Science and Engineering, 2007, vol. 129, no. 4, s. 770-779.
  • [5] Dong J., Subrahmanyam K., Wong Y., Hong G., Mohanty A., Bayesian-inference-based Neural Networks for Tool Wear Estimation, International Journal of Advanced Manufacturing Technology, 2006, vol. 30, s. 797-807.
  • [6] El-Wardany T. I., Gao D., Elbestawi M.A., Tool Condition Monitoring in Drilling Using Vibration Signature Analysis, International Journal of Machine Tools and Manufacture, 1996, vol. 36, s. 687-711.
  • [7] Ghosh N., Ravib Y.B., Patrac A., Mukhopadhyayc S., Pauld S., Estimation of Tool Wear During CNC Milling Using NN-based Sensor Fusion, Mechanical Systems and Signal Processing, 2007, no. 21, s. 466-479.
  • [8] Grzesik W., Bernat P., An Investigation of the Cutting Process for Chip Breaking Monitoring in Turning of Steels, Journal of Manufacturing Science and Engineering, 1998, vol. 120, no. 3, s. 555-562.
  • [9] Jemielniak K., Detection of Cutting Edge Breakage in Turning, CIRP Annals, 1992, vol. 41, no. l, s. 97-100.
  • [10] Jemielniak K., Some Aspects of Acoustic Emission Signal Pre-processing, Journal of Material Processing Technology, 2001, vol. 109, s. 242-247.
  • [11] Jemielniak K., Some Aspects of AE Application in Tool Condition Monitoring, Ultrasonics, 2000, vol. 38, s. 604-608.
  • [12] Jemielniak K., Bombinski S., Hierarchical Strategies in Tool Wear Monitoring, w: Proc. IMechE 2006, vol. 220/B, s. 375-381.
  • [13] Jemielniak K., Bombinski S., Aristimuno P.X., Tool Condition Monitoring in Micromilling Based on Hierarchical Integration of Signal Measures, CIRP Annals, 2008, vol. 57, no. l, s. 121-124.
  • [14] Jemielniak K., Kwiatkowski L., Wrzosek P., Diagnosis of Tool Wear Based on Cutting Forces and AE Measures as Inputs to Neural Network, Journal of Intelligent Manufacturing, 1998, vol. 9, s. 447-455.
  • [15] Jemielniak K., Kossakowska J., Urbański T., Application of wavelet transform of acoustic, emission and cutting force signals for tool condition, monitoring in rough turning of Inconel 625, 2010, w: Proc. IMechE, vol. 225, part B: J. Engineering Manufacture, DOI: 10.1243/09544054JEM2057.
  • [16] Jemielniak K., Otman O., Catastrophic Tool Failure Detection Based on AE Signal Analysis, CIRP Annals, 1998, vol. 47, no. 1, s. 31-34.
  • [17] Kamarthi S., Pittner S., Fourier and Wavelet Transform for Flank Wear Estimation, Mechanical Systems and Signal Processing, 1997, vol. 11, no. 6, s. 791-809.
  • [18] Kannatey-Asibu E., Dornfeld D., A Study of Tool Wear Using Statistical Analysis of Metal Cutting Acoustic Emission, Wear, 1982, vol. 76, s. 247-261.
  • [19] Kunpeng Z., Wong Y.S., Hong G.S., Wavelet Analysis of Sensor Signals for Tool Condition Monitoring - A Review and Some New Results, International Journal of Machine Tools and Manufacture, 2009, vol. 49, s. 537-553.
  • [20] Li X., A Brief Review - Acoustic Emission Method for Tool Wear Monitoring in Turning, International Journal of Machine Tools and Manufacture, 2002, vol. 42. s. 157-165.
  • [21] Li X., Ouyang G., Liang Z., Complexity Measure of Motor Current Signals for Tool Flute Breakage Detection in End Milling, International Journal of Machine Tools and Manufacture, 2008, vol. 48, s. 371-379.
  • [22] Marinescu I., Axinte D., A Time-frequency AE-based Monitoring Technique to Identify Workpiece Surface Malfunctions in Milling with Multiple Teeth Cutting Simultaneously, International Journal of Machine Tools and Manufacture, 2009, vol. 49, s. 53-65.
  • [23] Peng Y., Empirical Model Decomposition Based Time-frequency Analysis for the Effective Detection of Tool Breakage, Journal of Manufacturing Science and Engineering, 2006, vol. 128, no. l, s. 154-166.
  • [24] Quan Y., Zhoub M., Luo Z., On-line Robust Identification of Tool Wear Via Multi-sensor NN Fusion, Engineering Applications of Artificial Intelligence, 1998, vol. 11, s. 717-722.
  • [25] Rene de Jesus R.T., Gilberto H.R., Ivan T.V., Carlos J.C.J., FPGA Based On-line Tool Breakage Detection System for CNC Milling Machines, Mechatronics, 2004, vol. 14, s. 439-454.
  • [26] Salgado D., Alonso F., Tool Wear Detection in Turning Operations Using Singular Spectrum Analysis, Journal of Materials Processing Technology, 2006, vol. 171, s. 451-458.
  • [27] Salgado D., Alonso F., Cambero I., Marcelo A., In-Process Surface Roughness Prediction System Using Cutting Vibrations in Turning, International Journal of Advanced Manufacturing Technology, 2009, vol. 43, s. 40-51.
  • [28] Scheffer C., Heyns P.C., Wear Monitoring in Turning Operations Using Vibration and Strain Measurements, Mechanical Systems and Signal Processing, 2001, vol. 15, no. 6, s. 1185-1202.
  • [29] Scheffer C., Heyns P.C., An Industrial Tool Wear Monitoring System for Interrupted Turning, Mechanical Systems and Signal Processing, 2004, vol. 18, s. 1219-1242.
  • [30] Shi D., Gindy N.N., Tool Wear Predictive Model Based on Least Squares Support Vector Machines, Mechanical Systems and Signal Processing, 2007, vol. 21, s. 1799-1814.
  • [31] Sick B., On-line and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: A Review of More Than a Decade of Research, Mechanical Systems and Signal Processing, 2002, vol. 16, no. 4, s. 487-546.
  • [32] Sun J., Hong G.S., Rahman M., Wong Y.S., Identification of Feature Set for Effective Tool Condition Monitoring by AE Sensing, International Journal of Production Research, 2004, vol. 42, no. 5, s. 901-918.
  • [33] Teti R., Jawahir I.S., Jemielniak K., Segreto T., Chen S., Kossakowska J., Chip Form Monitoring through Advanced Processing of Cutting Force Sensor Signals, CIRP Annals, 2006, vol. 55, no. l, s. 75-80.
  • [34] Teti R., Jemielniak K., O'Donnell G., Dornfeld D., Advanced monitoring of machining operations (keynote paper), CIRP Annals-Manufacturing Technology, 2010, vol. 59, no. 2, s. 717-739.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP2-0016-0046
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