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Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network

Year 2020, Volume: 1 Issue: 2, 59 - 68, 29.12.2020

Abstract

Cutting force is one of most important criteria for evaluating machinability of workpieces. For this purpose, in present study, prediction of cutting forces obtained by turning AISI 1050 steel with cryo-treated and untreated CVD-coated cutting tool inserts with artificial neural networks (ANN) was investigated. Machining parameters such as feed rate, cutting speed and conditions of cutting tool insert were selected. These parameters were used for input parameters while cutting force was used for output parameter. The employed ANN structure was chosen according to network type, training function, adaption learning function and performance function as feed-forward back propagation, TRAINLM, LEARNGD and MSE, respectively. Thus, the estimation values of cutting forces attained from ANN model during training and experimental values coincide perfectly with the regression lines, which make the R2 = 0.99874 in training. For this reason, cutting force was explained by ANN with an acceptable accuracy in this study.

Supporting Institution

Batman University Scientific Research Projects Unit

Project Number

BTÜBAP-2019-YL-07

Thanks

Many thanks to BTUBAP for financial support.

References

  • J. Kratochvíl, J. Petrů, M. Pagáč, J. Holubják, and J. Mrazik, “Effect of Chip Breakers on The Cutting Force During The Machining of Steel C45.” Advances in Science and Technology Research Journal, vol. 11, no. 1, pp. 173-178, 2017.
  • B. Yılmaz, Ş. Karabulut, and A. Güllü, “Performance analysis of new external chip breaker for efficient machining of Inconel 718 and optimization of the cutting parameters.” Journal of Manufacturing Processes, vol. 32, pp. 553-563, 2018.
  • Ş. Baday, H. Başak, and A. Güral, “Analysis of spheroidized AISI 1050 steel in terms of cutting forces and surface quality.” Kovove Mater., vol. 54, pp. 315-320, 2016.
  • Ş. Baday, “Küreselleştirme ısıl işlemi uygulanmış AISI 1050 çeliğin tornalanmasında esas kesme kuvvetlerinin yapay sinir ağları ile modellenmesi.” Technological Applied Sciences, vol. 11, no. 1, pp. 1-9, 2016.
  • M. Hanief, , M.F. Waniand, and M.S. Charoo, “Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis.” Engineering science and technology, an international journal, vol. 20, no. 3, pp. 1220-1226, 2017.
  • H. Gürbüz, F. Sönmez, Ş. BADAY, and U. Şeker, “Farklı Talaş Kırıcı Formlarının Esas Kesme Kuvvetlerine Etkisinin Matematiksel Modellenmesi.” Batman Üniversitesi Yaşam Bilimleri Dergisi, vol. 8, no. 2/2, pp. 13-21, 2018.
  • H. Başak, ve Ş. Baday, “Küreselleştirilmiş orta karbonlu bir çeliğin işlenmesinde, kesme parametrelerinin kesme kuvvetleri ve yüzey pürüzlülüğüne etkilerinin regresyon analizi ile modellenmesi.” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 22, no 4, pp. 253-258, 2016.
  • S. Yagmur, A. Kurt, and U. Seker, “Evaluation and mathematical modeling of delamination and cutting forces in milling of carbon fiber reinforced composite (CFRP) materials.” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 35, no. 1, pp. 457-465, 2020.
  • H.B. Ulas, and M.T. Ozkan, “Turning processes investigation of materials austenitic, martensitic and duplex stainless steels and prediction of cutting forces using artificial neural network (ANN) techniques.” Indian Journal of Engineering and Materials Sciences, vol. 26, no. 2, pp. 93-104, 2019.
  • G. Uzun, and İ. Çiftçi, “Ç 5140 çeliğinin mekanik özelliklerinin takım aşınması ve kesme kuvvetlerine etkisinin incelenmesi.” Politeknik Dergisi, vol. 15, no. 1, pp. 29-34, 2012.
  • A.İ. Özkan, İ. Sarıtaş, and S. Yaldız, “Tornalama İşleminde Kesme Kuvvetlerinin ve Takım Ucu Sıcaklığının Yapay Sinir Ağı ile Tahmin Edilmesi, 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09), Karabük, Türkiye, 2009, pp. 13-15.
  • A. Kurt, S. Sürücüler, ve A. Kirik, “Kesme Kuvvetlerinin Tahmini İçin Matematiksel Bir Model Geliştirme.” Politeknik Dergisi, vol. 13, no. 1, pp. 15-20, 2010.
  • S. Jeyakumar, K. Marimuthu, and T. Ramachandran, “Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM.” Journal of Mechanical Science and Technology, vol. 27, no. 9, pp. 2813-2822, 2013.
  • F. Kara, K. Aslantas, and A. Çiçek, “ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel.” Neural Computing and Applications, vol. 26, no 1, pp. 237-250, 2015.
  • I. Asilturk, H. Kahramanli, H.E. Mounayri, “Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel.” Materials Science and Technology, vol. 28, no. 8, pp. 980-986, 2012.
  • N.A. Özbek, A. Çiçek, M. Gülesin, and O Özbek, “Investigation of the effects of cryogenic treatment applied at different holding times to cemented carbide inserts on tool wear.” International Journal of Machine Tools and Manufacture, vol. 86, pp. 34-43, 2014.
  • A.D. Shirbhate, N.V. Deshpande, and Y.M. Puri, “Effect of cryogenic treatment on cutting torque and surface finish in drilling operation with AISI M2 high speed steel.” Int. J. Mech. Eng. Rob. Res, vol. 1, no. 2, pp. 50-58, 2012.
  • D. Candane, N. Alagumurthi, and K. Palaniradja, “Effect of cryogenic treatment on microstructure and wear characteristics of AISI M35 HSS.” Int J Mater Sci App, vol 2, no. 2, pp. 56–65, 2013.
  • S. Akincioğlu, H. Gökkaya, and İ. Uygur, “A review of cryogenic treatment on cutting tools.” The International Journal of Advanced Manufacturing Technology, vol. 78, no 9-12, pp. 1609-1627, 2015.
Year 2020, Volume: 1 Issue: 2, 59 - 68, 29.12.2020

Abstract

Project Number

BTÜBAP-2019-YL-07

References

  • J. Kratochvíl, J. Petrů, M. Pagáč, J. Holubják, and J. Mrazik, “Effect of Chip Breakers on The Cutting Force During The Machining of Steel C45.” Advances in Science and Technology Research Journal, vol. 11, no. 1, pp. 173-178, 2017.
  • B. Yılmaz, Ş. Karabulut, and A. Güllü, “Performance analysis of new external chip breaker for efficient machining of Inconel 718 and optimization of the cutting parameters.” Journal of Manufacturing Processes, vol. 32, pp. 553-563, 2018.
  • Ş. Baday, H. Başak, and A. Güral, “Analysis of spheroidized AISI 1050 steel in terms of cutting forces and surface quality.” Kovove Mater., vol. 54, pp. 315-320, 2016.
  • Ş. Baday, “Küreselleştirme ısıl işlemi uygulanmış AISI 1050 çeliğin tornalanmasında esas kesme kuvvetlerinin yapay sinir ağları ile modellenmesi.” Technological Applied Sciences, vol. 11, no. 1, pp. 1-9, 2016.
  • M. Hanief, , M.F. Waniand, and M.S. Charoo, “Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis.” Engineering science and technology, an international journal, vol. 20, no. 3, pp. 1220-1226, 2017.
  • H. Gürbüz, F. Sönmez, Ş. BADAY, and U. Şeker, “Farklı Talaş Kırıcı Formlarının Esas Kesme Kuvvetlerine Etkisinin Matematiksel Modellenmesi.” Batman Üniversitesi Yaşam Bilimleri Dergisi, vol. 8, no. 2/2, pp. 13-21, 2018.
  • H. Başak, ve Ş. Baday, “Küreselleştirilmiş orta karbonlu bir çeliğin işlenmesinde, kesme parametrelerinin kesme kuvvetleri ve yüzey pürüzlülüğüne etkilerinin regresyon analizi ile modellenmesi.” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 22, no 4, pp. 253-258, 2016.
  • S. Yagmur, A. Kurt, and U. Seker, “Evaluation and mathematical modeling of delamination and cutting forces in milling of carbon fiber reinforced composite (CFRP) materials.” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 35, no. 1, pp. 457-465, 2020.
  • H.B. Ulas, and M.T. Ozkan, “Turning processes investigation of materials austenitic, martensitic and duplex stainless steels and prediction of cutting forces using artificial neural network (ANN) techniques.” Indian Journal of Engineering and Materials Sciences, vol. 26, no. 2, pp. 93-104, 2019.
  • G. Uzun, and İ. Çiftçi, “Ç 5140 çeliğinin mekanik özelliklerinin takım aşınması ve kesme kuvvetlerine etkisinin incelenmesi.” Politeknik Dergisi, vol. 15, no. 1, pp. 29-34, 2012.
  • A.İ. Özkan, İ. Sarıtaş, and S. Yaldız, “Tornalama İşleminde Kesme Kuvvetlerinin ve Takım Ucu Sıcaklığının Yapay Sinir Ağı ile Tahmin Edilmesi, 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09), Karabük, Türkiye, 2009, pp. 13-15.
  • A. Kurt, S. Sürücüler, ve A. Kirik, “Kesme Kuvvetlerinin Tahmini İçin Matematiksel Bir Model Geliştirme.” Politeknik Dergisi, vol. 13, no. 1, pp. 15-20, 2010.
  • S. Jeyakumar, K. Marimuthu, and T. Ramachandran, “Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM.” Journal of Mechanical Science and Technology, vol. 27, no. 9, pp. 2813-2822, 2013.
  • F. Kara, K. Aslantas, and A. Çiçek, “ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel.” Neural Computing and Applications, vol. 26, no 1, pp. 237-250, 2015.
  • I. Asilturk, H. Kahramanli, H.E. Mounayri, “Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel.” Materials Science and Technology, vol. 28, no. 8, pp. 980-986, 2012.
  • N.A. Özbek, A. Çiçek, M. Gülesin, and O Özbek, “Investigation of the effects of cryogenic treatment applied at different holding times to cemented carbide inserts on tool wear.” International Journal of Machine Tools and Manufacture, vol. 86, pp. 34-43, 2014.
  • A.D. Shirbhate, N.V. Deshpande, and Y.M. Puri, “Effect of cryogenic treatment on cutting torque and surface finish in drilling operation with AISI M2 high speed steel.” Int. J. Mech. Eng. Rob. Res, vol. 1, no. 2, pp. 50-58, 2012.
  • D. Candane, N. Alagumurthi, and K. Palaniradja, “Effect of cryogenic treatment on microstructure and wear characteristics of AISI M35 HSS.” Int J Mater Sci App, vol 2, no. 2, pp. 56–65, 2013.
  • S. Akincioğlu, H. Gökkaya, and İ. Uygur, “A review of cryogenic treatment on cutting tools.” The International Journal of Advanced Manufacturing Technology, vol. 78, no 9-12, pp. 1609-1627, 2015.
There are 19 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Şehmus Baday 0000-0003-4208-8779

Onur Ersöz 0000-0002-9792-2268

Project Number BTÜBAP-2019-YL-07
Publication Date December 29, 2020
Submission Date August 21, 2020
Published in Issue Year 2020 Volume: 1 Issue: 2

Cite

APA Baday, Ş., & Ersöz, O. (2020). Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network. Journal of Soft Computing and Artificial Intelligence, 1(2), 59-68.
AMA Baday Ş, Ersöz O. Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network. JSCAI. December 2020;1(2):59-68.
Chicago Baday, Şehmus, and Onur Ersöz. “Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel With Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network”. Journal of Soft Computing and Artificial Intelligence 1, no. 2 (December 2020): 59-68.
EndNote Baday Ş, Ersöz O (December 1, 2020) Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network. Journal of Soft Computing and Artificial Intelligence 1 2 59–68.
IEEE Ş. Baday and O. Ersöz, “Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network”, JSCAI, vol. 1, no. 2, pp. 59–68, 2020.
ISNAD Baday, Şehmus - Ersöz, Onur. “Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel With Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network”. Journal of Soft Computing and Artificial Intelligence 1/2 (December 2020), 59-68.
JAMA Baday Ş, Ersöz O. Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network. JSCAI. 2020;1:59–68.
MLA Baday, Şehmus and Onur Ersöz. “Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel With Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network”. Journal of Soft Computing and Artificial Intelligence, vol. 1, no. 2, 2020, pp. 59-68.
Vancouver Baday Ş, Ersöz O. Estimation of Cutting Forces Obtained by Machining AISI 1050 Steel with Cryo-Treated and Untreated Cutting Tool Insert by Using Artificial Neural Network. JSCAI. 2020;1(2):59-68.