Farklı Kesme Parametreleri ve MQL Debilerinde Elde Edilen Deneysel Değerlerin S/N Oranları ve YSA ile Analizi

Yıl 2021, , 1093 - 1107, 01.09.2021

Hüseyin Gürbüz , Yunus Emre Gönülaçar

https://doi.org/10.2339/politeknik.833833

Cited By: 3

Öz

Bu çalışmada, AISI 4140 çeliğinin tornalanması işleminde kesme hızı, ilerleme oranı ve MQL debisinin esas kesme kuvvetleri (Fc) ve ortalama yüzey pürüzlülüğüne (Ra) etkisi hem deneysel hem de istatiksel olarak incelenmiştir. Bu doğrultuda deney sonuçlarının değerlendirilmesinde sinyal/gürültü (S/N) oranları ve yapay sinir ağları (YSA) kullanılmıştır. İşleme deneylerinde, kesme parametreleri olarak üç farklı kesme hızı (75, 100, 125 m/dk), üç farklı ilerleme oranı (0,16 - 0,25 – 0,5 mm/dev), üç farklı MQL debisi (0,35 - 0,8 - 1,7 ml/dk) ve sabit kesme derinliği (2,5 mm) seçilmiştir. İşleme deneylerinde MQL debi artışının Fc üzerinde Ra’ya göre daha etkili olduğu tespit edilmiştir. Ayrıca tüm MQL debi uygulamalarında hem Fc hem de Ra’nın ilerleme oranı ile arttığı ve kesme hızı ile genel olarak azaldığı görülmüştür. Fc ve Ra için S/N oranları ve YSA ile elde edilen R2 değerleri R2S/N(Fc)= 0,9996, R2S/N(Ra)= 0,9984, R2YSA(Fc)=0,9990 ve R2YSA(Ra)=0,9884 bulunmuştur. S/N oranlarına göre Fc ve Ra üzerindeki en etkili kontrol faktörlerinin sırasıyla; ilerleme oranı, kesme hızı ve MQL debi olduğu belirlenmiştir. Elde edilen regresyon değerlerine bağlı olarak S/N oranlarının ve YSA’nın deneysel verileri yüksek güven aralığında tahmin etmede geçerli olduğu tespit edilmiştir.

Anahtar Kelimeler

YSA, S/N oranı, minimum miktarda yağlama (MQL), esas kesme kuvveti, yüzey pürüzlülüğü

Destekleyen Kurum

Batman Ü;niversitesi

Proje Numarası

BTÜBAP-2017-Yüksek Lisans-2

Teşekkür

Yazarlar, (BTÜBAP-2017-Yüksek Lisans-2) numaralı proje ile bu araştırmaya sağladığı mali desteklerden dolayı Batman Üniversitesi Bilimsel Araştırma Projeleri Birimine ve laboratuvar imkânlarından faydalandığımız Gazi Üniversitesi Teknoloji Fakültesine teşekkür ederiz.

Analysis of Experimental Values Obtained at Different Cutting Parameters and MQL Flows with S/N Ratios and ANN

Öz

In this study, the effect of cutting speed, feed rate and MQL flow rate on main cutting forces (Fc) and surface roughness (Ra) in turning process of AISI 4140 steel was investigated both experimentally and statistically. Accordingly, signal/noise (S/N) ratios and artificial neural networks (ANN) were used to evaluate the experimental results. As cutting parameters in machining experiments, three different cutting speeds (75, 100, 125 m/min), three different feed rates (0.16 - 0.25 - 0.5 mm/rev), three different MQL flow rates (0.35 - 0.8 - 1.7 ml/min) and a constant depth of cut (2.5 mm) were selected. In machining experiments, it was determined that the increase in MQL flow rate is more effective on Fc than Ra. It was also seen that both Fc and Ra increased with the feed, and generally decreased with the cutting speed in all MQL flow rate applications. According to S/N ratios, it was determined that the most effective control factors on Fc and Ra are feed rate, cutting speed and MQL flow rate, respectively. Depending on the regression values obtained, it was determined that S/N ratios and ANN are valid in predicting experimental data at high confidence intervals.

Anahtar Kelimeler

ANN, S/N ratio, minimum quantity lubrication (MQL), main cutting force, surface roughness

Proje Numarası

BTÜBAP-2017-Yüksek Lisans-2

Kaynakça

• [1] Sivaiah P., Chakradhar D., “Modeling and optimization of sustainable manufacturing process in machining of 17-4 PH stainless steel”, Measurement, 134: 142-152, (2019).
• [2] Venkatesan K., Devendiran S., Sachin D., Swaraj J., “Investigation of machinability characteristics and comparative analysis under different machining conditions for sustainable manufacturing”, Measurement, 154: 107425, (2020).
• [3] Zaman P. B., Dhar, N. R., “Design and evaluation of an embedded double jet nozzle for MQL delivery intending machinability improvement in turning operation”, Journal of Manufacturing Processes, 44: 179-196, (2019).
• [4] Kaladhar M., “Evaluation of hard coating materials performance on machinability issues and material removal rate during turning operations”, Measurement, 135: 493-502, (2019).
• [5] Viswanathan R., Ramesh S., Subburam V., “Measurement and optimization of performance characteristics in turning of Mg alloy under dry and MQL conditions”, Measurement, 120: 107-113, (2018).
• [6] Yıldırım Ç. V., Sarıkaya M., Kıvak T., Şirin, Ş., “The effect of addition of hBN nanoparticles to nanofluid-MQL on tool wear patterns, tool life, roughness and temperature in turning of Ni-based Inconel 625”, Tribology International, 134: 443-456, (2019).
• [7] Das A., Patel S. K., Biswal B. B., Sahoo N., Pradhan A., “Performance evaluation of various cutting fluids using MQL technique in hard turning of AISI 4340 alloy steel”, Measurement, 150: 107079, (2020).
• [8] Dutta S., Narala, S. K. R., “Optimizing turning parameters in the machining of AM alloy using Taguchi methodology”, Measurement, 169: 108340, (2021).
• [9] Özbek N. A., “Effects of cryogenic treatment types on the performance of coated tungsten tools in the turning of AISI H11 steel”, Journal of Materials Research and Technology, 9(4): 9442-9456, (2020).
• [10] Baday Ş., Başak H., Sönmez F., “The assessment of cutting force with taguchi design in medium carbon steel–applied spheroidization heat treatment”, Measurement and Control, 50(4): 89-96, (2017).
• [11] Akgün M., Demir, H., Çiftçi, İ., “Mg2Si partikül takviyeli magnezyum alaşımlarının tornalanmasında yüzey pürüzlülüğünün optimizasyonu”, Politeknik Dergisi, 21(3): 645-650, (2018).
• [12] Mia M., Dhar, N. R., “Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network”, Measurement, 92: 464-474, (2016).
• [13] Hanief M., Wani M. F., Charoo, M. S., “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, 20(3): 1220-1226, (2017).
• [14] Kara F., Karabatak M., Ayyıldız, M., Nas, E., “Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting”, Journal of Materials Research and Technology, 9(1): 969-983, (2020).
• [15] Cica D., Sredanovic B., Lakic-Globocki G., Kramar D., “Modeling of the cutting forces in turning process using various methods of cooling and lubricating: an artificial intelligence approach”, Advances in Mechanical Engineering, 5: 798597, (2013).
• [16] Mia M., Khan M. A., Dhar N. R., “Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool”, The International Journal of Advanced Manufacturing Technology, 93(1-4): 975-991, (2017).
• [17] Badiger P. V., Desai V., Ramesh M. R., Prajwala B. K., Raveendra, K., “Cutting forces, surface roughness and tool wear quality assessment using ANN and PSO approach during machining of MDN431 with TiN/AlN-coated cutting tool”, Arabian Journal for Science and Engineering, 44(9): 7465-7477, (2019).
• [18] Baday Ş., “Küreselleştirme Isıl İşlemi Uygulanmış Aısı 1050 Çeliğin Tornalanmasında Esas Kesme Kuvvetlerinin Yapay Sinir Ağları ile Modellenmesi”, Technological Applied Sciences, 11(1): 1-9, (2016).
• [19] 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”, Journal of Soft Computing and Artificial Intelligence, 1(2): 1-10, (2020).
• [20] Gürbüz H., Sözen A., Şeker U., “Modelling of effects of various chip breaker forms on surface roughness in turning operations by utilizing artificial neural networks”, Politeknik Dergisi, 19(1): 71-83. (2016).
• [21] Mitsubishi Carbide, “Guide to turning inserts”, Japan, (2019).
• [22] STN ISO 3685, “Tool-Life Testing with Single-Point Turning Tools”, (1999).
• [23] Mia M., Dey P. R., Hossain M. S., Arafat M. T., Asaduzzaman, M., Ullah, M. S., Zobaer, S. T., “Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition”, Measurement, 122: 380-391, (2018).
• [24] Abhang L. B., Hameedullah M., “Experimental investigation of minimum quantity lubricants in alloy steel turning”, International Journal of Engineering Science and Technology, 2(7): 3045-3053, (2010).
• [25] Rahim E. A., İbrahim M. R., Rahim A. A., Aziz S., Mohid Z., “Experimental Investigation of Minimum Quantity Lubrication (MQL) as a Sustainable Cooling Technique”, Procedia CIRP, 26: 351-354, (2015).
• [26] Saini A., Dhiman S., Sharma R., Setia S., “Experimental estimation and optimization of process parameters under minimum quantity lubrication and dry turning of AISI-4340 with different carbide inserts”, Journal of Mechanical Science and Technology, 28(6): 2307-2318, (2014).
• [27] Hwang Y. K., Lee C. M., “Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments”, Journal of Mechanical Science and Technology, 24(8): 1669-1677, (2010). [28] Hadad M., Sadeghi B., “Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy”, Journal of Cleaner Production, 54: 332-343, (2013).
• [29] Dhar N. R., Ahmed, M. T., Islam, S., “An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel”, International Journal of Machine Tools & Manufacture, 47: 748-753, (2007).
• [30] Ji X., Li B., Zhang X., Liang S. Y., “The Effects of Minimum Quantity Lubrication (MQL) on Machining Force, Temperature, and Residual Stress”, Internatıonal Journal of Precision Engineering and Manufacturing, 15(11): 2443-2451, (2014).
• [31] Behera B. C., Ghosh S., Rao P. V., “Modeling of cutting force in MQL machining environment considering chip tool contact friction”, Tribology International, 117: 283-295, (2018).
• [32] Hagiwara M., Chen S., Jawahir I. S., “Contour finish turning operations with coated grooved tools: Optimization of Machining Performance”, Journal of Materials Processing Technology, 209(1): 332-342, (2009).
• [33] Sarıkaya M., Güllü A., “Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL”, Journal of Cleaner Production, 65: 604-616, (2014).
• [34] Hemaid A., Tawfeek T., Ibrahim A. A., “Experimental investigation on surface finish during turning of aluminum under dry and minimum quantity lubrication machining conditions”, American Journal of Materials Engineering and Technology, 4(1): 1-5, (2016).
• [35] Kumar S., Singh D., Kalsi N. S., “Analysis of Surface Roughness during Machining of Hardened AISI 4340 Steel using Minimum Quantity lubrication”, Materials Today: Proceedings, 4: 3627–3635, (2017).
• [36] Paturi U. M. R., Maddu Y. R., Maruri R. R., Narala S. K. R., “Measurement and analysis of surface roughness in WS2 solid lubricant assisted minimum quantity lubrication (MQL) turning of Inconel 718”, Procedia CIRP, 40: 138–143, (2016).
• [37] Sarıkaya M., Güllü A., “Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25”, Journal of Cleaner Production, 91: 347-357, (2015).
• [38] Çakır A., Yağmur S., Kavak N., Küçüktürk G., Şeker U., “The effect of minimum quantity lubrication under different parameters in the turning of AA7075 and AA2024 aluminium alloys”, International Journal of Advanced Manufacturing Technology, 84: 2515-2521, (2016).
• [39] Diniz A. E., Ferreira J. R., Filho F. T., “Influence of refrigeration/lubrication condition on SAE 52100 hardened steel turning at several cutting speeds”, International Journal of Machine Tools & Manufacture, 43: 317–326. (2003).
• [40] Sahoo A. K., Sahoo B., “Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach)”. Measurement, 46(8): 2868-2884, (2013).