Kesme Parametrelerinin Haynes 242 Nikel Bazlı Süper Alaşım Malzemenin Tornalamasında Güç Tüketimi Üzerindeki Etkilerinin RSM ve GA ile İncelenmesi

Yıl 2022, Cilt: 22 Sayı: 5, 1204 - 1213, 27.10.2022

Hakan Kazan , Engin Ufuk Ergül

https://doi.org/10.35414/akufemubid.1103662

Cited By: 2

Öz

Talaşlı imalatta yaygın kullanımı nedeniyle tornalama işleminde güç tüketimini azaltmak sürdürülebilir bir üretim süreci için kilit faktörlerden biridir. Nikel bazlı süper alaşımlar, üstün mekanik özelliklerinden nedeniyle endüstride sıklıkla tercih edilirler. Bu çalışmanın amacı işlem parametrelerinin Haynes 242 nikel bazlı süper alaşım malzemenin tornalanmasında güç tüketimi üzerine etkilerinin incelenmesidir. Bu kapsamda, yanıt yüzeyi yöntemi (Response Surface Method-RSM) ile birleştirilen üç seviye Box-Behnken tasarımı ve genetik algoritma (GA) uygulanarak minimum güç tüketiminin tahmin edilmesinde kullanılan optimum parametre değerlerini belirlemek için regresyon modeli oluşturulmuştur. İlk olarak 3 farklı seviyedeki takım uç radüsü (0.4,0.6 ve 0.8 mm), talaş derinliği (0.2,0.4 ve 0.6 mm), ve ilerleme oranı (0.1,0.2 ve 0.3 mm/rev.) dikkate alınarak Box-Behnken deney tasarımı oluşturulmuştur. Ardından, elde edilen deney setlerine göre AdvantEdge™ vasıtasıyla her bir deney setine ait güç tüketimleri ölçülmüştür. Sonrasında, RSM’den elde edilen matematiksel tahmin modelinden yararlanılarak güç tüketimi tahmini için GA kullanılmıştır. Sonuç olarak, bu yöntemlerle bulunan tahmin değerleri karşılaştırılmış ve birbirlerine çok yakın oldukları görülmüştür. Hem istatistiksel hem de simülasyon programı sonuçları, güç tüketimini minimize etmek için düşük ilerleme oranı ve talaş derinliğine ihtiyaç duyulduğunu göstermektedir.

Anahtar Kelimeler

Tornalama, Nikel Esaslı Süperalaşım, RSM, Box Behnken, Genetik Algoritma, Güç Tüketimi

Investigation the Effects of Cutting Parameters on Power Consumption in Turning of Haynes 242 Nickel-Based Super Alloy by RSM and GA

Öz

Due to its widespread use in machining, reducing power consumption in the turning process is one of the key factors for a sustainable production process. Nickel-based superalloys are preferred in variable applications due to their superior mechanical properties. This study aims to investigate the effects of process parameters on power consumption in turning of Haynes 242 nickel-based superalloy. In this context, three levels of Box-Behnken design combined with the Response Surface Method (RSM) and genetic algorithm (GA) were applied to find the optimum parameter values used in the estimation of the minimum power consumption to create the regression model. First, the Box-Behnken experimental design was created based on 3 different levels of tool nose radius (0.4,0.6 and 0.8 mm), depth of cut (0.2,0.4 and 0.6 mm), and feed rate (0.1,0.2 and 0.3 mm/rev.). Then, the power consumption of each test measured by AdvantEdge™ based on the obtained experimental sets. Then, GA was used for power consumption estimation by utilizing the mathematical estimation model obtained from RSM. Finally, the estimated values obtained by both methods were compared. Both statistical and simulation results show that low feed rate and depth of cut are needed to minimize power consumption.

Anahtar Kelimeler

Turning, Nickel-Based Superalloy, RSM, Box Behnken, Genetic Algorithm, Power Consumption

Kaynakça

• Alvarez, M., Ilzarbe, L., Viles, E. and Tanco, M., 2009. The use of genetic algorithms in response surface methodology. Quality Technology & Quantitative Management, 6(3), 295-307.
• Aydın, K., Akgün, A., Yavaş, Ç., Gök, A. and Şeker, U., 2021. Experimental and Numerical Study of Cutting Force Performance of Wave Form End Mills on Gray Cast Iron. Arabian Journal for Science and Engineering, 46(12), 12299-12307.
• Aydin, K., Katmer, S., Gok, A. and 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.
• Chen, S.-H. and Tsai, K.-T., 2017. The study of plasma-assisted machining to Inconel-718. Advances in Mechanical Engineering, 9(12), 1687814017735789.
• Choudhury, I. and El-Baradie, M., 1998. Machinability of nickel-base super alloys: a general review. Journal of Materials Processing Technology, 77(1-3), 278-284.
• Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., Hauschild, M. and Kellens, K., 2012. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals, 61(2), 587-609.
• Dymek, S., Wróbel, M., Dollar, M. and Blicharski, M., 2006. Influence of plastic deformation and prolonged ageing time on microstructure of a Haynes 242 alloy. Journal of Microscopy, 224(1), 24-26.
• Ergul, E. U. and Eminoglu, I., 2020. Power-law fitness scaling on multi-objective evolutionary algorithms: interpretations of experimental results. Soft Computing, 24(5), 3893-3907.
• Ergül, E. U., Gezegin, C. and YILDIZ, A., 2019. Yanıt yüzey yöntemi ve genetik algoritma kullanılarak transformatör sargı en sıcak nokta sıcaklığının modellenmesi ve optimizasyonu. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 467-480.
• Esmaeilpour, R., Kim, H., Park, T., Pourboghrat, F., Agha, A. and Abu-Farha, F., 2020. Effect of hardening law and process parameters on finite element simulation of single point incremental forming (SPIF) of 7075 aluminum alloy sheet. Mechanics & Industry, 21(3), 302.
• Habeeb, H., Abou-El-Ho, K., Mohamad, B., Ghani, J. A. and Kadirgama, K., 2008. Investigating of tool wear, tool life and surface roughness when machining of nickel alloy 242 with using of different cutting tools. Asian Journal of Scientific Research, 1(3), 222-230.
• Kılıçkap, E. and Hüseyinoğlu, M., 2010. Tepki yüzey modeli ve genetik algoritma kullanılarak AISI 316’nın delinmesinde oluşan çapak yüksekliğinin modellenmesi ve optimizasyonu. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 1(1), 71-80.
• Kribes, N., Hessainia, Z. and Yallese, M. A., 2015. Optimisation of machining parameters in hard turning by desirability function analysis using response surface methodology. In Design and Modeling of Mechanical Systems-II (pp. 73-81): Springer.
• Kttagawa, T. and Maekawa, K., 1990. Plasma hot machining for new engineering materials. Wear, 139(2), 251-267.
• Leshock, C. E., Kim, J.-N. and Shin, Y. C., 2001. Plasma enhanced machining of Inconel 718: modeling of workpiece temperature with plasma heating and experimental results. International Journal of Machine Tools and Manufacture, 41(6), 877-897.
• Ma, J., Ge, X., Chang, S. and Lei, S., 2014. Assessment of cutting energy consumption and energy efficiency in machining of 4140 steel. The International Journal of Advanced Manufacturing Technology, 74(9), 1701-1708.
• Mativenga, P. and Rajemi, M., 2011. Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Annals, 60(1), 149-152. Mori, M., Fujishima, M., Inamasu, Y. and Oda, Y., 2011. A study on energy efficiency improvement for machine tools. CIRP Annals, 60(1), 145-148.
• Öktem, H., Erzurumlu, T. and Kurtaran, H., 2005. Application of response surface methodology in the optimization of cutting conditions for surface roughness. Journal of Materials Processing Technology, 170(1-2), 11-16.
• Özlü, B. and 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. Panwar, V., Sharma, D. K., Kumar, K. P., Jain, A. and Thakar, C., 2021. Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. Materials Today: Proceedings, 46, 6474-6481.
• Parida, A. K., 2019. Analysis of chip geometry in hot machining of Inconel 718 alloy. Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, 43(1), 155-164.
• Parida, A. K. and Maity, K., 2018. Experimental investigation on tool life and chip morphology in hot machining of Monel-400. Engineering Science and Technology, an International Journal, 21(3), 371-379.
• Parida, A. K. and Maity, K., 2019. Numerical and experimental analysis of specific cutting energy in hot turning of Inconel 718. Measurement, 133, 361-369.
• Pérez, J., Llorente, J. and Sanchez, J., 2000. Advanced cutting conditions for the milling of aeronautical alloys. Journal of Materials Processing Technology, 100(1-3), 1-11.
• Rao, B., Dandekar, C. R. and Shin, Y. C., 2011. An experimental and numerical study on the face milling of Ti–6Al–4V alloy: Tool performance and surface integrity. Journal of Materials Processing Technology, 211(2), 294-304.
• Sangwan, K. S. and Kant, G., 2017. Optimization of machining parameters for improving energy efficiency using integrated response surface methodology and genetic algorithm approach. Procedia CIRP, 61, 517-522.
• Shrot, A. and Bäker, M., 2012. Determination of Johnson–Cook parameters from machining simulations. Computational Materials Science, 52(1), 298-304.
• Srivastava, S., 1992. A Low-Thermal Expansion, High Strength Ni–Mo–Cr Alloy for Gas Turbines. Superalloys, 92, 227-236.
• Suresh, P., Rao, P. V. and Deshmukh, S., 2002. A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 42(6), 675-680.
• Uğur, L., 2019. 7075 Alüminyum Malzemesinin Frezelenmesinde Yüzey Pürüzlülüğünün Yanıt Yüzey Metodu İle Optimizasyonu. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12(1), 326-335.
• Uğur, L., 2022. TI–6AL–4V Sıcak İşlenmesi Üzerine Etkilerinin Sonlu Elemanlar Yöntemi ile İncelenmesi. Mühendislik Bilimleri ve Tasarım Dergisi, 10(2), 532-537.
• Venkatesan, K., Ramanujam, R. and Kuppan, P., 2017. Investigation of machinability characteristics and chip morphology study in laser-assisted machining of Inconel 718. The International Journal of Advanced Manufacturing Technology, 91(9), 3807-3821.
• Venkatesh, G. and Chakradhar, D., 2017. Influence of thermally assisted machining parameters on the machinability of Inconel 718 superalloy. Silicon, 9(6), 867-877.
• Vijayan, K., Ranjithkumar, P. and Shanmugarajan, B., 2018. Comparison of Response Surface Methodology and Genetic Algorithm in parameter optimization of laser welding process. Applied Mathematics & Information Sciences, 12(1), 239-248.
• Yuan, C., Zhai, Q. and Dornfeld, D., 2012. A three dimensional system approach for environmentally sustainable manufacturing. CIRP Annals, 61(1), 39-42.
• İnternet Kaynakları 1. http://www.haynesintl.com/242site/ (2005).