Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 11 Sayı: 1, 82 - 93, 13.03.2024
https://doi.org/10.31202/ecjse.1350095

Öz

Proje Numarası

Project No. 2215-D-10

Kaynakça

  • [1] E. Hamamcı. Frezeleme İşlemlerinde takım Ömrünün akustik emisyon sinyalleri İle akıllı yöntemler kullanılarak belirlenmesi. Master’s thesis, Süleyman Demirel University, 2004.
  • [2] H. Sağlam. Frezelemede Yapay Sinir Ağları Kullanarak, Çok-Elemanlı Kuvvet Ölçümlerine Dayalı Takım Durumu İzleme. PhD thesis, Selçuk University, 2000.
  • [3] X. Li, S. Dong, and Z. Yuan. Discrete wavelet transform for tool breakage monitoring. International Journal of Machine Tools & Manufacture, 39:1935–1944, 1999.
  • [4] A. Al-Habaibeh and N. Gindy. A new approach for systematic design of condition monitoring systems for milling processes. Journal of Materials Processing Technology, 107:243–251, 2000.
  • [5] X. Li. A brief review: Acoustic emission method for tool wear monitoring during turning. International Journal of Machine Tools and Manufacture, 42:157–165, 2002.
  • [6] O. Çolak. CNC Freze Tezgahı İçin Kesme Parametrelerinin Akıllı Yöntemlerle Elektronik Ortamda Optimizasyonu. PhD thesis, Süleyman Demirel University, 2006.
  • [7] X. Chen and B. Li. Acoustic emission method for tool condition monitoring based on wavelet analysis. The International Journal of Advanced Manufacturing Technology, 33:968–976, 2007.
  • [8] F. J. Alonso and D. R. Salgado. Analysis of structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing, 22:735–748, 2008.
  • [9] P. Bhattacharyya, D. Sengupta, S. Mukhopadhyay, and A. B. Chattopadhyay. On-line tool condition monitoring in face milling using current and power signals. International Journal of Production Research, 46:1187–1201, 2008.
  • [10] A. K. Gupta. Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. International Journal of Production Research, 48:763–778, 2010.
  • [11] S. Zhang, J. F. Li, J. Sun, and F. Jiang. Tool wear and cutting forces variation in high-speed end-milling ti-6al-4v alloy. The International Journal of Advanced Manufacturing Technology, 46:69–78, 2010.
  • [12] U. Zuperl, F. Cus, and J. Balic. Intelligent cutting tool condition monitoring in milling. Journal of Achievements in Materials and Manufacturing Engineering, 49:477–486, 2011.
  • [13] S. Korenaga, T. Uematsu, H. Ohsawa, Y. Itoh, H. Shizuka, and K. Sakai. Evaluation of tool wear in end milling of ti-6al-4v alloy using cutting force analysis- relationship between cutting force and tool wear on various cutting conditions. Journal of the Japan Society for Precision Engineering, 83:439–444, 2017.
  • [14] K. Tiwari, A. Shaik, and N. Arunachalam. Tool wear prediction in end milling of ti-6al-4v through kalman filter based fusion of texture features and cutting forces. Procedia Manufacturing, 26:1459–1470, 2018.
  • [15] U. Çaydaş. Ti6Al4V Alaşımının Elektro Erozyon ve Elektro Kimyasal İşleme Yöntemleriyle İşlenebilirliğinin Araştırılması. PhD thesis, Fırat University, 2008.
  • [16] O. Çakır, M. Kıyak, and E. Altan. Titanyum ve alaşımlarının talaşlı Şekillendirilmesi. In II. Makine Tasarım ve İmalat Teknolojileri Kongresi, pages 21–30, 2003.
  • [17] H. Akkuş. Tornalama İşlemlerinde yüzey pürüzlülüğünün İstatistiksel ve yapay zeka yöntemleriyle tahmin edilmesi. Master’s thesis, Selçuk University, 2010.
  • [18] S. Palanisamy, S. D. McDonal, and M. S. Dargusch. Effects of coolant pressure on chip formation while turning ti6al4v. International Journal of Machine Tools and Manufacture, 49:739–743, 2009.
  • [19] A. K. Nandy, M. C. Gowrishankar, and S. Paul. Some studies on high-pressure cooling in turning of ti-6al-4v. International Journal of Machine Tools and Manufacture, 49:182–198, 2009.
  • [20] E. O. Ezugwu. Effect of high-pressure coolant supply when machining nickel-base, inconel 718, alloy with coated carbide tools. Journal of Materials Processing Technology, 153:1045–1050, 2004.
  • [21] E. O. Ezugwu. Key improvements in the machining of difficult-to-cut aerospace superalloys. International Journal of Machine Tools and Manufacture, 45:1353–1367, 2005.
  • [22] C. Courbon, D. Kramar, P. Krajnik, F. Pusavec, J. Rech, and J. Kopac. Investigation of machining performance in high-pressure jet assisted turning of inconel 718: An experimental study. International Journal of Machine Tools and Manufacture, 49:1114–1125, 2009.
  • [23] O. Güngör. Kesici takım titreşimlerinin gerçek zamanlı İzlenmesi. Master’s thesis, Süleyman Demirel University, 2010
  • [24] İ. B. Toprak. Karmaşık Yüzeylerin İşlenmesinde Çok Sensörlü Çevrimiçi Durum İzleme ve Kontrol. PhD thesis, Süleyman Demirel University, 2013.
  • [25] H. Koçak and G. Çetin. The diagnosis of diabetes mellitus with boosting methods. El-Cezerî Journal of Science and Engineering, 10:409–419, 2023.
  • [26] A. D. Ertorsun, B. Bağ, G. Uzar, and M. A. Turanoğlu. Roc eğrisi yöntemi İle tanı testlerinin performanslarının değerlendirilmesi. 2022. ttp://tip.baskent.edu.tr/egitim/mezuniyetoncesi/calismagrp/ogrsmpzsnm12/10.2.pdf.
  • [27] B. Kaya. Sensör ve Karar Entegresyonu İle Frezeleme İşlemleri için Çevrimiçi Bir Takım Durum Gözlem Sisteminin Geliştirilmesi. PhD thesis, Kocaeli University, 2009.
  • [28] A. Salimiasl and M. Rafighi. Titreşim ve kesme kuvveti esaslı takım aşınmasının bulanık mantıkla İzlenmesi ve tahmini. Politeknik Dergisi, 20:111–120, 2017.
  • [29] İ. Gücüyener. Fuzzy based tool wear monitoring of the cnc milling machine. Yuzuncu Yil University Journal of the Institute of Natural & Applied Sciences, 27:248–256, 2022.
  • [30] L. Urtekin, F. Bozkurt, H. B. Özerkan, C. Çoğun, and İ. Uslan. The comparison of performance of electrolytic cu and cube tool electrodes in electric discharge machining of ti6al4v alloy. El-Cezerî Journal of Science and Engineering, 8: 1455–1461, 2021.

Determination of the Tool Wear using Cutting Force in Face Milling of Ti-6Al-4V under High Pressurized Cooling Conditions

Yıl 2024, Cilt: 11 Sayı: 1, 82 - 93, 13.03.2024
https://doi.org/10.31202/ecjse.1350095

Öz

In this study, plain and profile cutting operations were carried out in Ti-6Al-4V material with sharp and worn tools. In the experiments, the cutting speed was 90 [m/min], the feed rate was 0.1 [mm/tooth], and the pressure of the applied coolant was changed to 6, 100, 200, 300 bar, respectively. Haar wavelet was applied to the Fx force signals recorded during the experiments and decomposition was made at Level 5. As a result of the analysis of the obtained CA5 wavelet coefficients with the receiver operating characteristic (ROC) curve analysis, the limit values were determined to separate the worn tool from the unworn tool. In this way, it will be possible to stop the machine at the desired wear value.

Destekleyen Kurum

Süleyman Demirel University Scientific Research Projects Coordination Unit

Proje Numarası

Project No. 2215-D-10

Teşekkür

Tübitak 108M380. Blaser Swiss Lube. TUSAŞ-TAI and Süleyman Demirel University CAD-CAM Research and Application Center for their support in performing the study.

Kaynakça

  • [1] E. Hamamcı. Frezeleme İşlemlerinde takım Ömrünün akustik emisyon sinyalleri İle akıllı yöntemler kullanılarak belirlenmesi. Master’s thesis, Süleyman Demirel University, 2004.
  • [2] H. Sağlam. Frezelemede Yapay Sinir Ağları Kullanarak, Çok-Elemanlı Kuvvet Ölçümlerine Dayalı Takım Durumu İzleme. PhD thesis, Selçuk University, 2000.
  • [3] X. Li, S. Dong, and Z. Yuan. Discrete wavelet transform for tool breakage monitoring. International Journal of Machine Tools & Manufacture, 39:1935–1944, 1999.
  • [4] A. Al-Habaibeh and N. Gindy. A new approach for systematic design of condition monitoring systems for milling processes. Journal of Materials Processing Technology, 107:243–251, 2000.
  • [5] X. Li. A brief review: Acoustic emission method for tool wear monitoring during turning. International Journal of Machine Tools and Manufacture, 42:157–165, 2002.
  • [6] O. Çolak. CNC Freze Tezgahı İçin Kesme Parametrelerinin Akıllı Yöntemlerle Elektronik Ortamda Optimizasyonu. PhD thesis, Süleyman Demirel University, 2006.
  • [7] X. Chen and B. Li. Acoustic emission method for tool condition monitoring based on wavelet analysis. The International Journal of Advanced Manufacturing Technology, 33:968–976, 2007.
  • [8] F. J. Alonso and D. R. Salgado. Analysis of structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing, 22:735–748, 2008.
  • [9] P. Bhattacharyya, D. Sengupta, S. Mukhopadhyay, and A. B. Chattopadhyay. On-line tool condition monitoring in face milling using current and power signals. International Journal of Production Research, 46:1187–1201, 2008.
  • [10] A. K. Gupta. Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. International Journal of Production Research, 48:763–778, 2010.
  • [11] S. Zhang, J. F. Li, J. Sun, and F. Jiang. Tool wear and cutting forces variation in high-speed end-milling ti-6al-4v alloy. The International Journal of Advanced Manufacturing Technology, 46:69–78, 2010.
  • [12] U. Zuperl, F. Cus, and J. Balic. Intelligent cutting tool condition monitoring in milling. Journal of Achievements in Materials and Manufacturing Engineering, 49:477–486, 2011.
  • [13] S. Korenaga, T. Uematsu, H. Ohsawa, Y. Itoh, H. Shizuka, and K. Sakai. Evaluation of tool wear in end milling of ti-6al-4v alloy using cutting force analysis- relationship between cutting force and tool wear on various cutting conditions. Journal of the Japan Society for Precision Engineering, 83:439–444, 2017.
  • [14] K. Tiwari, A. Shaik, and N. Arunachalam. Tool wear prediction in end milling of ti-6al-4v through kalman filter based fusion of texture features and cutting forces. Procedia Manufacturing, 26:1459–1470, 2018.
  • [15] U. Çaydaş. Ti6Al4V Alaşımının Elektro Erozyon ve Elektro Kimyasal İşleme Yöntemleriyle İşlenebilirliğinin Araştırılması. PhD thesis, Fırat University, 2008.
  • [16] O. Çakır, M. Kıyak, and E. Altan. Titanyum ve alaşımlarının talaşlı Şekillendirilmesi. In II. Makine Tasarım ve İmalat Teknolojileri Kongresi, pages 21–30, 2003.
  • [17] H. Akkuş. Tornalama İşlemlerinde yüzey pürüzlülüğünün İstatistiksel ve yapay zeka yöntemleriyle tahmin edilmesi. Master’s thesis, Selçuk University, 2010.
  • [18] S. Palanisamy, S. D. McDonal, and M. S. Dargusch. Effects of coolant pressure on chip formation while turning ti6al4v. International Journal of Machine Tools and Manufacture, 49:739–743, 2009.
  • [19] A. K. Nandy, M. C. Gowrishankar, and S. Paul. Some studies on high-pressure cooling in turning of ti-6al-4v. International Journal of Machine Tools and Manufacture, 49:182–198, 2009.
  • [20] E. O. Ezugwu. Effect of high-pressure coolant supply when machining nickel-base, inconel 718, alloy with coated carbide tools. Journal of Materials Processing Technology, 153:1045–1050, 2004.
  • [21] E. O. Ezugwu. Key improvements in the machining of difficult-to-cut aerospace superalloys. International Journal of Machine Tools and Manufacture, 45:1353–1367, 2005.
  • [22] C. Courbon, D. Kramar, P. Krajnik, F. Pusavec, J. Rech, and J. Kopac. Investigation of machining performance in high-pressure jet assisted turning of inconel 718: An experimental study. International Journal of Machine Tools and Manufacture, 49:1114–1125, 2009.
  • [23] O. Güngör. Kesici takım titreşimlerinin gerçek zamanlı İzlenmesi. Master’s thesis, Süleyman Demirel University, 2010
  • [24] İ. B. Toprak. Karmaşık Yüzeylerin İşlenmesinde Çok Sensörlü Çevrimiçi Durum İzleme ve Kontrol. PhD thesis, Süleyman Demirel University, 2013.
  • [25] H. Koçak and G. Çetin. The diagnosis of diabetes mellitus with boosting methods. El-Cezerî Journal of Science and Engineering, 10:409–419, 2023.
  • [26] A. D. Ertorsun, B. Bağ, G. Uzar, and M. A. Turanoğlu. Roc eğrisi yöntemi İle tanı testlerinin performanslarının değerlendirilmesi. 2022. ttp://tip.baskent.edu.tr/egitim/mezuniyetoncesi/calismagrp/ogrsmpzsnm12/10.2.pdf.
  • [27] B. Kaya. Sensör ve Karar Entegresyonu İle Frezeleme İşlemleri için Çevrimiçi Bir Takım Durum Gözlem Sisteminin Geliştirilmesi. PhD thesis, Kocaeli University, 2009.
  • [28] A. Salimiasl and M. Rafighi. Titreşim ve kesme kuvveti esaslı takım aşınmasının bulanık mantıkla İzlenmesi ve tahmini. Politeknik Dergisi, 20:111–120, 2017.
  • [29] İ. Gücüyener. Fuzzy based tool wear monitoring of the cnc milling machine. Yuzuncu Yil University Journal of the Institute of Natural & Applied Sciences, 27:248–256, 2022.
  • [30] L. Urtekin, F. Bozkurt, H. B. Özerkan, C. Çoğun, and İ. Uslan. The comparison of performance of electrolytic cu and cube tool electrodes in electric discharge machining of ti6al4v alloy. El-Cezerî Journal of Science and Engineering, 8: 1455–1461, 2021.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik Uygulaması
Bölüm Makaleler
Yazarlar

İnayet Burcu Toprak 0000-0002-0894-5573

Oğuz Çolak 0000-0002-1777-9300

Mustafa Bayhan 0000-0001-5793-5390

Proje Numarası Project No. 2215-D-10
Yayımlanma Tarihi 13 Mart 2024
Gönderilme Tarihi 25 Ağustos 2023
Kabul Tarihi 18 Aralık 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 1

Kaynak Göster

IEEE İ. B. Toprak, O. Çolak, ve M. Bayhan, “Determination of the Tool Wear using Cutting Force in Face Milling of Ti-6Al-4V under High Pressurized Cooling Conditions”, ECJSE, c. 11, sy. 1, ss. 82–93, 2024, doi: 10.31202/ecjse.1350095.