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Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini

Yıl 2017, Cilt: 20 Sayı: 1, 111 - 120, 01.03.2017

Öz

Talaş kaldırma sürecinde, izlenmeyen takım aşınması parça bozukluğunu ve hurda sayısını artırmakla beraber, aynı zamanda takımın kırılmasına ve pahalı CNC takım tezgâhlarında yüksek hasarlara sebep olmaktadır. Aşınma oranına dayalı, takıma verilmesi gereken takım aşınma telafi değerlerinin doğru tespiti, takımda oluşan aşınmanın iş parçasının boyutlarında ve yüzey kalitesinde kabul edilemez bir sınıra geleceği anın bilinmesi ve kırılma olmadan önce yeni bir takımla değiştirilmesi için talaş kaldırma sürecini izleyen bir otomasyon takip sistemi gereklilik olmuştur. Bu çalışmada, talaşlı imalatta bir otomasyon sistemi kurmak ve yan yüzey takım aşınma miktarını anlık tahmin etmek için kuvvet ve titreşim algılayıcıları kullanılarak bir bulanık mantık sistemi tasarlanmıştır. Sistemin kurulması için, talaş kaldırma parametreleri, kesme kuvveti ve titreşim değişkenleri girdi olarak ve takım aşınma miktarı çıktı olarak bulanık mantık sistemine verilmiş. Taguchi metodu kullanılarak deney tasarımı yapılmıştır. Ölçülen ve tahmin edilen sonuçlar, takım aşınmasının tespiti için, bulanık mantık metodunun güvenilir olduğunu göstermiştir.

Kaynakça

  • [1] Zhu Kunpeng, W., HongGeokSoon, “Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. ”, International Journal of Machine Tools & Manufacture. 49: 537–553, (2009).
  • [2] Xiaoli Li, S. K. T., “Drill wear monitoring based on current signals”, Wear. 231: 172–178, (1999).
  • [3] Teti, R., Jemielniak, K., O'Donnell, G., et al., “Advanced monitoring of machining operations”, Cirp Annals-Manufacturing Technology. 59: 717-739, (2010).
  • [4] Jantunen, E., “A summary of methods applied to tool condition monitoring in Drilling”, International Journal of Machine Tools & Manufacture. 42: 997–1010, (2002).
  • [5] Liang, S. Y., Hecker, R. L.andLanders, R. G., “Machining process monitoring and control: The state-of-the-art”, Journal of Manufacturing Science and Engineering-Transactions of the Asme. 126: 297-310, (2004).
  • [6] Niu, Y., Wong, Y.andHong, G., “An intelligent sensor system approach for reliable tool flank wear recognition”, The International Journal of Advanced Manufacturing Technology. 14: 77-84, (1998).
  • [7] Dong, J., Subrahmanyam, K. V. R., Wong, Y. S., et al., “Bayesian-inference-based neural networks for tool wear estimation”, International Journal of Advanced Manufacturing Technology. 30: 797-807, (2006).
  • [8] U. Zuperl, F. C., J. Balic, “Intelligent cutting tool condition monitoring in milling”, Journal of Achievements in Materials and Manufacturing Engineering. 49: 477-486, (2011).
  • [9] Ertekin, Y. M., Kwon, Y.andTseng, T.-L., “Identification of common sensory features for the control of CNC milling operations under varying cutting conditions”, International Journal of Machine Tools and Manufacture. 43: 897-904, (2003).
  • [10] Chen, S.-L.and Jen, Y. W., “Data fusion neural network for tool condition monitoring in CNC milling machining”, International Journal of Machine Tools and Manufacture. 40: 381-400, (2000).
  • [11] Liu, Y.and Wang, C., “Neural network based adaptive control and optimization in the milling process”, International Journal of Advanced Manufacturing Technology. 15: 791-795, (1999).
  • [12] Yusuf, A., “In-process detection of tool breakages using time series monitoring of cutting forces”, International Journal of Machine Tools and Manufacture. 28: 157-172, (1988).
  • [13] Zhang, J.and Chen, J., “The development of an in-process surface roughness adaptive control system in end milling operations”, The International Journal of Advanced Manufacturing Technology. 31: 877-887, (2007).
  • [14] Benardos, P. G.and Vosniakos, G. C., “Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments”, Robotics and Computer-Integrated Manufacturing. 18: 343-354, (2002).
  • [15] Azouzi, R.and Guillot, M., “On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion”, International Journal of Machine Tools and Manufacture. 37: 1201-1217, (1997).
  • [16] El Ouafi, A., Guillot, M.andBedrouni, A., “Accuracy enhancement of multi-axis CNC machines through on-line neurocompensation”, Journal of Intelligent Manufacturing. 11: 535-545, (2000).
  • [17] Byrne, G., Dornfeld, D.andDenkena, B., “Advancing Cutting Technology”, CIRP Annals - Manufacturing Technology. 52: 483-507, (2003).
  • [18] Kilundu, B., Dehombreux, P.andChiementin, X., “Tool wear monitoring by machine learning techniques and singular spectrum analysis”, Mechanical Systems and Signal Processing. 25: 400-415, (2011).
  • [19] Ding, F.and He, Z., “Cutting tool wear monitoring for reliability analysis using proportional hazards model”, The International Journal of Advanced Manufacturing Technology. 57: 565-574, (2011).
  • [20] Noh, M.-S.and Hong, D., “Implementation of remote monitoring system for prediction of tool wear and failure using ART2”, Journal of Central South University of Technology. 18: 177-183, (2011).
  • [21] Zadeh, L. A., “Fuzzy sets”, Information and Control. 338–353, (1965).
  • [22] Amrita Sarkar, G. S. a. U. C. S., “APPLICATION OF FUZZY LOGIC IN TRANSPORT PLANNING”, International Journal on Soft Computing. 3: 1-21, (2012).
  • [23] Sharma, V., Sharma, S. K.andSharma, A., “Cutting tool wear estimation for turning”, Journal of Intelligent Manufacturing. 19: 99-108, (2008).

Monitoring and Estimating of Vibration and Cutting Force Based Tool Wear via Fuzzy Logic

Yıl 2017, Cilt: 20 Sayı: 1, 111 - 120, 01.03.2017

Öz

During the chip removal process, the unmonitored tool wear not only increase the number of scraped parts but also causes the untimely tool breakage and the high costly damage on the expensive CNC machine tools. For applying the correct tool offset on the cutting tool based on the wear ratios, for determining the critical tool wear rates influencing on the work piece dimensions and surface quality and for replacing a new tool with a worn one before tool breakage during the machining operations, an automation system is required for monitoring the operation accurately. For establishing the automation system for online monitoring and estimating the tool wear in this research, a fuzzy logic system is designed by using of cutting force and vibration sensors. Cutting parameters, cutting forces and vibration variables are applied as input and the wear rate as output data to the fuzzy logic for constructing the system. Taguchi method is applied to design an experimental table for carrying out the tests. The measured and estimated results confirm the reliability of the fuzzy logic method for tool wear estimation.

Kaynakça

  • [1] Zhu Kunpeng, W., HongGeokSoon, “Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. ”, International Journal of Machine Tools & Manufacture. 49: 537–553, (2009).
  • [2] Xiaoli Li, S. K. T., “Drill wear monitoring based on current signals”, Wear. 231: 172–178, (1999).
  • [3] Teti, R., Jemielniak, K., O'Donnell, G., et al., “Advanced monitoring of machining operations”, Cirp Annals-Manufacturing Technology. 59: 717-739, (2010).
  • [4] Jantunen, E., “A summary of methods applied to tool condition monitoring in Drilling”, International Journal of Machine Tools & Manufacture. 42: 997–1010, (2002).
  • [5] Liang, S. Y., Hecker, R. L.andLanders, R. G., “Machining process monitoring and control: The state-of-the-art”, Journal of Manufacturing Science and Engineering-Transactions of the Asme. 126: 297-310, (2004).
  • [6] Niu, Y., Wong, Y.andHong, G., “An intelligent sensor system approach for reliable tool flank wear recognition”, The International Journal of Advanced Manufacturing Technology. 14: 77-84, (1998).
  • [7] Dong, J., Subrahmanyam, K. V. R., Wong, Y. S., et al., “Bayesian-inference-based neural networks for tool wear estimation”, International Journal of Advanced Manufacturing Technology. 30: 797-807, (2006).
  • [8] U. Zuperl, F. C., J. Balic, “Intelligent cutting tool condition monitoring in milling”, Journal of Achievements in Materials and Manufacturing Engineering. 49: 477-486, (2011).
  • [9] Ertekin, Y. M., Kwon, Y.andTseng, T.-L., “Identification of common sensory features for the control of CNC milling operations under varying cutting conditions”, International Journal of Machine Tools and Manufacture. 43: 897-904, (2003).
  • [10] Chen, S.-L.and Jen, Y. W., “Data fusion neural network for tool condition monitoring in CNC milling machining”, International Journal of Machine Tools and Manufacture. 40: 381-400, (2000).
  • [11] Liu, Y.and Wang, C., “Neural network based adaptive control and optimization in the milling process”, International Journal of Advanced Manufacturing Technology. 15: 791-795, (1999).
  • [12] Yusuf, A., “In-process detection of tool breakages using time series monitoring of cutting forces”, International Journal of Machine Tools and Manufacture. 28: 157-172, (1988).
  • [13] Zhang, J.and Chen, J., “The development of an in-process surface roughness adaptive control system in end milling operations”, The International Journal of Advanced Manufacturing Technology. 31: 877-887, (2007).
  • [14] Benardos, P. G.and Vosniakos, G. C., “Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments”, Robotics and Computer-Integrated Manufacturing. 18: 343-354, (2002).
  • [15] Azouzi, R.and Guillot, M., “On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion”, International Journal of Machine Tools and Manufacture. 37: 1201-1217, (1997).
  • [16] El Ouafi, A., Guillot, M.andBedrouni, A., “Accuracy enhancement of multi-axis CNC machines through on-line neurocompensation”, Journal of Intelligent Manufacturing. 11: 535-545, (2000).
  • [17] Byrne, G., Dornfeld, D.andDenkena, B., “Advancing Cutting Technology”, CIRP Annals - Manufacturing Technology. 52: 483-507, (2003).
  • [18] Kilundu, B., Dehombreux, P.andChiementin, X., “Tool wear monitoring by machine learning techniques and singular spectrum analysis”, Mechanical Systems and Signal Processing. 25: 400-415, (2011).
  • [19] Ding, F.and He, Z., “Cutting tool wear monitoring for reliability analysis using proportional hazards model”, The International Journal of Advanced Manufacturing Technology. 57: 565-574, (2011).
  • [20] Noh, M.-S.and Hong, D., “Implementation of remote monitoring system for prediction of tool wear and failure using ART2”, Journal of Central South University of Technology. 18: 177-183, (2011).
  • [21] Zadeh, L. A., “Fuzzy sets”, Information and Control. 338–353, (1965).
  • [22] Amrita Sarkar, G. S. a. U. C. S., “APPLICATION OF FUZZY LOGIC IN TRANSPORT PLANNING”, International Journal on Soft Computing. 3: 1-21, (2012).
  • [23] Sharma, V., Sharma, S. K.andSharma, A., “Cutting tool wear estimation for turning”, Journal of Intelligent Manufacturing. 19: 99-108, (2008).
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Aydin Salımıasl Bu kişi benim

Mohammad Rafıghı Bu kişi benim

Yayımlanma Tarihi 1 Mart 2017
Gönderilme Tarihi 4 Haziran 2016
Yayımlandığı Sayı Yıl 2017 Cilt: 20 Sayı: 1

Kaynak Göster

APA Salımıasl, A., & Rafıghı, M. (2017). Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini. Politeknik Dergisi, 20(1), 111-120.
AMA Salımıasl A, Rafıghı M. Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini. Politeknik Dergisi. Mart 2017;20(1):111-120.
Chicago Salımıasl, Aydin, ve Mohammad Rafıghı. “Titreşim Ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi Ve Tahmini”. Politeknik Dergisi 20, sy. 1 (Mart 2017): 111-20.
EndNote Salımıasl A, Rafıghı M (01 Mart 2017) Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini. Politeknik Dergisi 20 1 111–120.
IEEE A. Salımıasl ve M. Rafıghı, “Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini”, Politeknik Dergisi, c. 20, sy. 1, ss. 111–120, 2017.
ISNAD Salımıasl, Aydin - Rafıghı, Mohammad. “Titreşim Ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi Ve Tahmini”. Politeknik Dergisi 20/1 (Mart 2017), 111-120.
JAMA Salımıasl A, Rafıghı M. Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini. Politeknik Dergisi. 2017;20:111–120.
MLA Salımıasl, Aydin ve Mohammad Rafıghı. “Titreşim Ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi Ve Tahmini”. Politeknik Dergisi, c. 20, sy. 1, 2017, ss. 111-20.
Vancouver Salımıasl A, Rafıghı M. Titreşim ve Kesme Kuvveti Esaslı Takım Aşınmasının Bulanık Mantıkla İzlenmesi ve Tahmini. Politeknik Dergisi. 2017;20(1):111-20.
 
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