Investigation of Cutting Forces in Turning of AISI 316L Stainless Steel with Experimental and Finite Element Analysis on Prediction with Artificial Neural Networks

Year 2023, Volume: 12 Issue: 3, 111 - 124, 31.12.2023

Barış Özlü Levent Uğur

Abstract

In this study, the effects of machining parameters on cutting force in turning AISI 316L stainless steel are experimentally investigated. In addition, it is aimed to compare the cutting forces predicted by the finite element method (FEM) and Artificial Neural Network (ANN) with the experimental results. Experimental findings and finite element analysis were conducted at three different levels of cutting speed, feed rate and depth of cut. It was done in the ThirdWave AdvantEdge program, which is a finite element analysis software. Regression and ANN methods were used to predict cutting forces with ANN. As a result, according to experimental, FEM, Regression and ANN results, the optimum machining parameters were determined as 0.8 mm cutting depth, 170 m/min cutting speed and 0.12 mm/rev feed rate. In the experiments, the lowest cutting force was measured as 260.1 N. It has been determined that the obtained experimental results and FEM, Regression and ANN results overlap and are acceptable.

Keywords

AISI 316L, turning, finite element analysis, ANN

AISI 316L Paslanmaz Çeliğinin Tornalanmasında Kesme Kuvvetlerinin Deneysel ve Sonlu Elemanlar Analiziyle Araştırılması Yapay Sinir Ağları ile Tahmin Edilmesi

Abstract

Bu çalışmada, AISI 316L paslanmaz çeliğin tornalanmasında kesme kuvveti üzerine işleme parametrelerinin etkileri deneysel olarak araştırmaktır. Ayrıca yapılan sonlu elemanlar yöntemi (SEY) ve Yapay Sinir Ağ (YSA) ile tahmin edilen kesme kuvvetlerinin deneysel sonuçlarla karşılaştırılması hedeflenmiştir. Deneysel bulgular ve sonlu elemanlar analizi kesme hızının, ilerleme miktarının ve kesme derinliğinin üç farklı seviyesinde yapılmıştır. Sonlu elemanlar analiz yazılımı olan ThirdWave AdvantEdge programında yapılmıştır. YSA ile kesme kuvvetlerinin tahmininde Regresyon ve YSA metotlarından yararlanılmıştır. Sonuç olarak, deneysel, SEY, Regresyon ve YSA sonuçlarına göre optimum işleme parametrelerinin 0,8 mm kesme derinliği, 170 m/dak kesme hızı ve 0,12 mm/dev ilerleme miktarı olarak belirlenmiştir. Deneylerde en düşük kesme kuvveti 260,1 N ölçülmüştür. Elde edilen deneysel sonuçları ile SEY, Regresyon ve ANN sonuçlarının örtüştüğü ve kabul edilebilir olduğu tespit edilmiştir.

Keywords

AISI 316L, tornalama, sonlu elemanlar analizi, YSA

References

• Akgün, M. (2022). AISI H13 sıcak iş takım çeliğinin işlenmesinde kesme kuvvetinin deneysel, nümerik ve istatistiksel olarak incelenmesi. Journal of the Institute of Science and Technology, 12(3), 1758-1769.
• Akkök, M., Acar, B., & Açmaz, E. (2013). Experimental analysis and wear modeling for mechanical components of a typical rail launcher. Wear, 306(1-2), 1-9. In.
• Aydin, K., Katmer, S., Gok, A., & Seker, U. (2021). Experimental and statistical investigation of the machining performance of wave form end mills on AISI 316L stainless steel. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(4), 2225-2238.
• Aydın, K., Katmer, Ş., Gök, A., & Şeker, U. (2021). Experimental and statistical investigation of the machining performance of wave form end mills on AISI 316L stainless steel. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(4), 2225-2238.
• Aydin, K., & Kazan, H. (2023). AISI 316L Alaşımın Tel Erozyon Yöntemi ile İşlenmesinde Kesme Parametrelerinin Yüzey Kalitesine Etkisi. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 575-584.
• Barış, Ö. (2021). Sleipner soğuk iş takım çeliğinin tornalanmasında kesme parametrelerinin kesme kuvveti, yüzey pürüzlülüğü ve talaş şekli üzerine etkisinin incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(3), 1241-1252.
• Bharasi, N. S., Pujar, M., Das, C., Philip, J., Thyagarajan, K., Paneerselvi, S., Moitra, A., Chandramouli, S., Karki, V., & Kannan, S. (2019). Microstructure, corrosion and mechanical properties characterization of AISI type 316L (N) stainless steel and modified 9Cr-1Mo steel after 40,000 h of dynamic sodium exposure at 525° C. Journal of Nuclear Materials, 516, 84-99.
• Camuşcu, N. (2006). Effect of cutting speed on the performance of Al2O3 based ceramic tools in turning nodular cast iron. Materials & Design, 27(10), 997-1006.
• Ciftci, I. (2006). Machining of austenitic stainless steels using CVD multi-layer coated cemented carbide tools. Tribology International, 39(6), 565-569.
• Davim, J. P., Gaitonde, V., & Karnik, S. (2008). Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. Journal of materials processing technology, 205(1-3), 16-23.
• Djavanroodi, F., Omranpour, B., & Sedighi, M. (2013). Artificial neural network modeling of ECAP process. Materials and Manufacturing Processes, 28(3), 276-281.
• Dorogoy, A., & Rittel, D. (2009). Determination of the Johnson–Cook material parameters using the SCS specimen. Experimental mechanics, 49(6), 881. In.
• Galanis, N., & Manolakos, D. E. (2014). Finite element analysis of the cutting forces in turning of femoral heads from AISI 316l stainless steel. Proceedings of the World Congress on Engineering,
• Guo, Y., & Dornfeld, D. (2000). Finite element modeling of burr formation process in drilling 304 stainless steel. J. Manuf. Sci. Eng., 122(4), 612-619.
• Gürbüz, H., Kafkas, F., & Şeker, U. (2012). AISI 316L Çeliğinin İşlenmesinde Kesici Takım Kesici Kenar Formu ve Talaş Kırıcı Formlarının Kesme Kuvvetleri Ve Yüzey Pürüzlülüğü Üzerine Etkisi. Batman Üniversitesi Yaşam Bilimleri Dergisi, 1(2), 173-184.
• Gürbüz, H., Şeker, U., & Kafkas, F. (2020). Effects of cutting tool forms on the surface integrity in turning of AISI 316L stainless steel. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1), 225-240.
• Johnson, G. R. A. c. m. a. d. f. m. s. t. l. s., high strain rates, and high temperatures. Proc. 7th Inf. Sympo. Ballistics, 541-547. In.
• Kara, F., Aslantaş, K., & Cicek, A. (2016). Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Applied Soft Computing, 38, 64-74.
• Korkmaz, M. E., & Günay, M. (2018). Finite Element Modelling of Cutting Forces and Power Consumption in Turning of AISI 420 Martensitic Stainless Steel. Arabian Journal for Science & Engineering (Springer Science & Business Media BV), 43(9).
• Kumar, K. K., & Choudhury, S. (2008). Investigation of tool wear and cutting force in cryogenic machining using design of experiments. Journal of materials processing technology, 203(1-3), 95-101.
• MacKay, D. J. (1992). Bayesian interpolation. Neural computation, 4(3), 415-447.
• Maranhão, C., & Davim, J. P. (2010). Finite element modelling of machining of AISI 316 steel: numerical simulation and experimental validation. Simulation modelling practice and theory, 18(2), 139-156.
• Mebrahitom, A., Choon, W., & Azhari, A. (2017). Side milling machining simulation using finite element analysis: prediction of cutting forces. Materials Today: Proceedings, 4(4), 5215-5221.
• Niesłony, P., Grzesik, W., Chudy, R., & Habrat, W. (2015). Meshing strategies in FEM simulation of the machining process. Archives of Civil and Mechanical Engineering, 15(1), 62-70.
• Oussama, B., Yapan, Y. F., Uysal, A., Abdelhakim, C., & Mourad, N. (2023). Assessment of turning AISI 316L stainless steel under MWCNT-reinforced nanofluid-assisted MQL and optimization of process parameters by NSGA-II and TOPSIS. The International Journal of Advanced Manufacturing Technology, 1-14.
• Özlü, B., & Uğur, L. (2021). Optimization of cutting forces on turning of Ti-6Al-4V Alloy by 3D FEM simulation analysis. Journal of Engineering Research and Applied Science, 10(2), 1789-1795.
• Parihar, R. S., Sahu, R. K., & Srinivasu, G. (2017). Finite element analysis of cutting forces generated in turning process using deform 3D software. Materials Today: Proceedings, 4(8), 8432-8438.
• Rai, J. K., & Xirouchakis, P. (2009). FEM-based prediction of workpiece transient temperature distribution and deformations during milling. The International Journal of Advanced Manufacturing Technology, 42, 429-449.
• Raju, B. P., & Swamy, M. K. (2012). Finite element simulation of a friction drilling process using deform-3D. International Journal of Engineering Research and Applications, 2(6), 716-721.
• Ramezani, M., & Afsari, A. (2015). Surface roughness and cutting force estimation in the CNC turning using artificial neural networks. Management Science Letters, 5(4), 357-362.
• Ranganathan, S., Senthilvelan, T., & Sriram, G. (2010). Evaluation of machining parameters of hot turning of stainless steel (Type 316) by applying ANN and RSM. Materials and Manufacturing Processes, 25(10), 1131-1141.
• Tesler, A. B., Kim, P., Kolle, S., Howell, C., Ahanotu, O., & Aizenberg, J. (2015). Extremely durable biofouling-resistant metallic surfaces based on electrodeposited nanoporous tungstite films on steel. Nature communications, 6(1), 8649.
• Tounsi, N., Vincenti, J., Otho, A., & Elbestawi, M. (2002). From the basic mechanics of orthogonal metal cutting toward the identification of the constitutive equation. International Journal of Machine Tools and Manufacture, 42(12), 1373-1383.
• Uysal, A., Demiren, F., & Altan, E. (2016). Investigation of surface roughness and chip forms in milling of stainless steel by MQL method. Acta Physica Polonica A, 129(4), 439-441.
• Yakubu, S. I., Mohammed, S. A., & Ogheneblorhie, C. O. (2020). Characterization and corrosion behaviours of AISI 316 in hydrochloric environment at various concentrations. Zaštita materijala, 61(3), 220-228.
• Yan, H., Hua, J., & Shivpuri, R. (2007). Flow stress of AISI H13 die steel in hard machining. Materials & design, 28(1), 272-277. In.
• Yaşar, S. A. (2020). 17-4 PH ve 15-5 PH paslanmaz çeliklerinin tornalanmasında kesme parametrelerinin kesme kuvveti ve yüzey pürüzlülüğüne etkilerinin araştırılması. Karaelmas Fen ve Mühendislik Dergisi, 10(1), 71-81.