CNC Freze Tezgahının Bulanık Tabanlı Takım Aşınma Takibi

Yıl 2022, Cilt: 27 Sayı: 2, 248 - 256, 30.08.2022

İsmet Gücüyener

https://doi.org/10.53433/yyufbed.1067638

Öz

Talaşlı imalat sistemlerinde kesici takım aşınması, hassas imalat süreçlerinde hatalara neden olur. Hatalı üretim de işlenen hammadde israfına ve boşuna harcanan zaman kaybına neden olur. Takım aşınmasının sürekli izlenmesi ve aşınma değerinin tolerans değerinin dışına çıkması durumunda otomatik uyarı verilmesi bu sorunları çözecektir. Titreşim değerleri ve motorların çektiği güçler, üretim sırasında kesici takım aşınmasının temassız izlenmesinde önemli ipuçları sağlar. Bu çalışmada düşük maliyetli sensörlerin kullanımı ve uygulanan bulanık karar mekanizması sayesinde kesici takım durumu yüzde 90,17 doğrulukla çevrimiçi olarak tespit edilebilmiştir. İş mili motorunun çektiği gücün RMS değeri, fiber optik sensör çıkış voltajının ortalama değeri ve seçilen fiber optik sensör çıkış dalgacık dönüşümlerinin ortalama değerleri tasarlanan sistemin girdileridir. Sistemin çıktısı, bulanık karar mekanizması tarafından tahmin edilen kesici takım aşınma değeridir.

Anahtar Kelimeler

Bulanık mantık, CNC freze, Fiber optik sensor, Wavelet dönüşümü

Fuzzy Based Tool Wear Monitoring of the CNC Milling Machine

Öz

In machining systems, cutting tool wear causes errors in precision manufacturing processes. It causes a waste of raw material processed in faulty production and a waste of time spent in vain. Continuous monitoring of tool wear and generating an automatic warning in case the wear value falls outside the tolerance value will resolve these issues. Vibration values and the powers drawn by the motors provide important clues in the non-contact monitoring of cutting tool wear during production. In this study, thanks to the use of low-cost sensors and the applied fuzzy decision mechanism , the cutting tool status could be detected online with an accuracy of 90.17 percent. The RMS value of the power drawn by the spindle motor, average value of fiber optic sensor output voltage, and the average values of selected fiber optic sensor output wavelet transformations are the inputs of the designed system. The output of the system is the cutting tool wear value estimated by the fuzzy decision mechanism.

Anahtar Kelimeler

CNC milling machine, Fiber optic sensor, Fuzzy inference, Wavelet transform

Kaynakça

• Abu-Mahfouz, I., Banerjee, A., & Rahman, A. H. M. E. (2016, November). Surface roughness identification in end milling using vibration signals and fuzzy clustering. ASME International Mechanical Engineering Congress and Exposition, Volume 4A: Dynamics, Vibration, and Control, 50541, V04AT05A060. doi.org/10.1115/IMECE2016-68207
• Axinte, D., & Gindy, N. (2003). Tool condition monitoring in broaching. Wear, 254(3–4), 370–382. doi:10.1016/S0043-1648(03)00003-6
• Besmir, C., & Kim, D-W. (2017). Fuzzy logic based tool condition monitoring for end-milling. Robotics and Computer–Integrated Manufacturing, 47, 22-36. doi: 10.1016/j.rcim.2016.12.009
• Dimla, E., & Lister, P. M. (2000). On-line metal cutting tool condition monitoring. I: Force and vibration analyses. International Journal of Machine Tools and Manufacture, 4, 739-768. doi:10.1016/S0890-6955(99)00084-X.
• Ertekin, Y. M., Kwon, Y., & Tseng (B) T. L. (2003). Identification of common sensory features for the control of cnc milling operations under varying cutting conditions. International Journal of Machine Tools & Manufacture, 43, 897-904. doi:10.1016/S0890-6955(03)00087-7
• Gücüyener, İ., & Emel, E. (2009). A fiber-optic bending sensor for the vibration monitoring of cnc face-milling machine. Solid State Phenomena, 147-149, 627-632. doi:10.4028/www.scientific.net/SSP.147-149.627
• Gücüyener, İ. (2018). Yüksek çözünürlüklü tasarlanan izleme yazilimi ile makine titreşim ölçümü. International Journal of Humanities and Art Research, 1, 20-25.
• Gücüyener, İ. (2021, November). Condition analysis of machines working in the production system. III. International Turkish World Engineering and Science Congress (Online)
• Jiang, C. Y., Zhang, Y. Z., & Xu H. J. (1987). In-process monitoring of tool wear stage by the frequency band-energy method. CIRP Annals, 36, 45-48. doi:10.1016/S0007-8506(07)62550-5.
• Jun, C-H., & Suh, S-H. (1999). Statistical tool breakage detection schemes based on vibration signals in cnc milling. International Journal of Machine Tools & Manufacture, 39(11), 1733–1746. doi:10.1016/S0890-6955(99)00028-0
• Karandikar, J., McLeay, T., Turner, S., & Schmitz, T. (2015). Tool wear monitoring using naïve bayes classifiers. The International Journal of Advanced Manufacturing Technology, 77, 1613–1626. doi.org/10.1007/s00170-014-6560-6
• Lee, B. Y., Liu, H. S., & Tarng, Y. S. (1997). Monitoring of tool fracture in end milling using induction motor current. Journal of Materials Processing Technology, 70, 279-284.
• Li, X., Djordjevich, A., & Venuvinod, P. K. (2000). Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring. IEEE Transactions on Industrial Electronics, 47(3), 697-702. doi:10.1109/41.847910
• Lin, X., Zhou, B., & Zhu, L. (2017). Sequential spindle current-based tool condition monitoring with support vector classifier for milling process. The International Journal of Advanced Manufacturing Technology, 92, 3319–3328. doi:10.1007/s00170-017-0396-9
• Merainani, B., Rahmoune, C., Benazzouz D., & Ould-Bouamama, B. (2016, November). Rollingbearing fault diagnosis based empirical wavelet transform using vibration signal. 8th International Conference on Modelling, Identification and Control (ICMIC). Algiers, Algeria: IEEE. doi: 10.1109/ICMIC.2016.7804169
• Rangwala, S., & Dornfield, D. (1990). Sensor integration using neural networks for intelligent tool condition monitoring. ASME Trans. Journal of Engineering for industry, 112(3), 219-228. doi:10.1115/1.2899578
• Susanto, V., & Chen, J. C. (2003). Fuzzy logic based in-process tool-wear monitoring system in face milling operations. The International Journal of Advanced Manufacturing Technology, 3, 186-192.
• Tatar, K., & Gren, P. (2008). Measurement of milling tool vibrations during cutting using laser vibrometry. International Journal of Machine Tools and Manufacture, 48, 380-387. doi:10.1016/j.ijmachtools.2007.09.009
• Trejo-Hernandez, M., & Osornio-Rios, R. A. (2018). Tool-wear estimation in cnc machine based on fusion vibration-current and neural network. Journal of Scientific & Industrial Research, 77, 688-691.
• Zhang, C., Yao, X., Zhang, J., & Jin, H. (2016). Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors, 16(6), 795-815. doi:10.3390/s16060795
• Zhang, J. Z., & Chen, J. C. (2008). Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. The International Journal of Advanced Manufacturing Technlogoy, 39(1), 118-128. doi:10.1007/s00170-007-1186-6
• Zhou, Y., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology, 96, 2509-2523. doi.org/10.1007/s00170-018-1768-5