MODELING OF SURFACE ROUGHNESS IN MILLING OF TI-6AL-4V ALLOY USING REGRESSION ANALYSIS

Yıl 2022, Cilt: 10 Sayı: 2, 620 - 630, 30.06.2022

İnayet Burcu Toprak Oğuz Çolak Mustafa Bayhan

https://doi.org/10.21923/jesd.886739

Öz

In this study, Ti-6Al-4V was machined under high pressure cooling conditions. Cutting parameters which were assumed as independent variables are consist of 4 different levels of cutting speed (Vc: 50-70-90-110 m/min), feed rate (f: 0.05-0.1-0.15-0.2 mm/rev) and cutting fluid pressure (P: 6-100-200-300 bar). By using SPSS 20 software, regression equations of surface roughness relative to cutting parameters was obtained as linear, second degree and linear logarithmic. Second degree multiple regression model showed best results of estimation. In the model, 95 percent of the surface roughness alterations can be explained by independent variables. Correlation between experimental data and the model was calculated as 0.975. As a result, second degree regression model proved to be successful in predicting surface roughness. The result of the study confirms the literature. When models are compared the most important parameter that affects surface roughness was observed as the feed rate. The results of the study confirms the literature.

Anahtar Kelimeler

Ti-6Al-4V, Surface Roughness, Regression Analysis, Milling

Destekleyen Kurum

Süleyman Demirel University Scientific Research Projects Coordination Unit

Proje Numarası

Project No. 2215-D-10

Teşekkür

We would like to thank to Tubitak 108M380, Blaser Swiss Lube, TUSAŞ-TAI and Süleyman Demirel University CAD-CAM Research and Application Center for their supports in performing the study.

Tİ-6AL-4V ALAŞIMININ FREZELENMESİNDE YÜZEY PÜRÜZLÜLÜĞÜNÜN REGRESYON ANALİZİ İLE MODELLENMESİ

Öz

Bu çalışmada, Ti-6Al-4V yüksek basınçlı soğutma şartlarında frezelenmiştir. Bağımsız değişken olarak kabul edilen kesme parametreleri; 4 farklı seviyedeki, kesme hızı (Vc: 50-70-90-110 m/dk), ilerleme oranı (f: 0.05-0.1-0.15-0.2 mm/diş) ve soğutma sıvısı basıncından (P: 6-100-200-300 bar) oluşmaktadır. SPSS 20 programı kullanılarak, yüzey pürüzlülüğü için kesme parametrelerine bağlı lineer, ikinci dereceden ve lineer logaritmik regresyon denklemleri elde edilmiştir. En iyi tahmin sonucunu ikinci dereceden çoklu regresyon modeli vermiştir. Modelde, yüzey pürüzlülüğündeki değişimin %95’ i bağımsız değişkenler tarafından açıklanabilmektedir. Deney verileri ve model arasındaki korelasyon 0,975 olarak hesaplanmıştır. Sonuç olarak, ikinci derece regresyon modelinin yüzey pürüzlülüğünü tahmin etmede başarılı olduğu kanıtlanmıştır. Modeller incelendiğinde, yüzey pürüzlülüğüne etki eden en önemli parametrenin, ilerleme oranı olduğu gözlenmiştir. Çalışmanın sonuçları literatürü doğrulamaktadır.

Anahtar Kelimeler

Ti-6Al-4V, Yüzey Pürüzlülüğü, Regresyon Analizi, Frezeleme

Proje Numarası

Project No. 2215-D-10

Kaynakça

• Akkuş, H., 2010. Prediction of Surface Roughness in Turning Operations Using Artificial Intelligence and Statistical Methods, MSc. Thesis, Selçuk University, Konya.
• Akkuş, H., Asiltürk, İ., 2011. Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models, Scientific Research and Essays, 6(13), 2729-2736.
• Akkuş H., Yaka H., Uğur L., 2017. Creating The Mathematical Model for The Surface Roughness Values Occurring During The Turning of The AISI1040 Steel, Sigma Journal of Engineering and Natural Sciences, 35 (2), 303-310.
• Akkuş, H., 2021. Investigation of Surface Roughness Values During Machinability of AISI 1040 Steel With Different Estimation Models, Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, 24 (2), 84-92.
• Arokiadass, R., Palaniradja, K., Alagumoorthi, N., 2011. Surface Roughness Prediction Model in End Milling of Al/SiCp MMC by Carbide Tools, International Journal of Engineering, Science and Technology, 3(6), 78-87.
• Asiltürk, İ., Çunkaş, M., 2011. Modeling and Prediction of Surface Roughness in Turning Operations Using Artificial Neural Network and Multiple Regression Method, Expert Systems with Applications, 38, 5826-5832.
• Asiltürk, İ., Akkuş, H., Demirci, M.T., 2012. Modelling of Surface Roughness Based on Vibration, Acoustic Emission and Cutting Parameters With Regression, Engineer and Machinery, 53, 55-62.
• Ay, M., Turhan, A., 2010. Investigation of The Effect Of Cutting Parameters On The Geometric Tolerances And Surface Roughness In Turning Operation, Electronic Journal of Machine Technologies, 7, 55-67.
• Ayyıldız, E.A., Ayyıldız M., Kara F., 2021, Optimization of Surface Roughness in Drilling Medium-Density Fiberboard with a Parallel Robot, Hindawi Advances in Materials Science and Engineering, Article ID 6658968, 8 pages.
• Çakır, O., Kıyak, M., Altan, E., 2003. Titanyum ve Alaşımlarının Talaşlı Şekillendirilmesi”, II. MakineTasarım ve İmalat Teknolojileri Kongresi, Konya, 21-30.
• Çaydaş, U., 2008. Investigation of the Machinability of Ti6Al4V Alloy by Electrical Discharge and Electrochemical Machining Processes, Phd Thesis, Fırat University, Elazığ.
• Çaydaş, U., Hasçalık, A., 2008. A study on Surface Roughness in Abrasive Waterjet Machining Process Using Artificial Neural Networks and Regression Analysis Method, Journal of Materials Processing Technology, 202, 574–582.
• Chavoshi, S.Z., Tajdari, M., 2010. Surface Roughness Modelling in Hard Turning Operation of AISI 4140 Using CBN Cutting Tool, Int J Mater Form, 3, 233–239.
• Ding, T., Zhang, S., Wang, Y., Zhu, X., 2010. Empirical Models and Optimal Cutting Parameters for Cutting Forces and Surface Roughness in Hard Milling of AISI H13 Steel, The International Journal of Advanced Manufacturing Technology, 51, 45-55.
• Fratila, D., Caizar, C., 2011. Application of Taguchi Method to Selection of Optimal Lubrication and Cutting Conditions in Face Milling of AlMg3, Journal of Cleaner Production, 19, 640-645.
• Hanief, M., Wani, M.F., 2016. Artificial Neural Network and Regression-Based Models for Prediction of Surface Roughness During Turning of Red Brass (C23000), Journal of Mechanical Engineering and Sciences (JMES), 10 (1), 1835-1845.
• Hascalık, A., Caydas, U., 2008. Optimization of Turning Parameters for Surface Roughness and Tool Life Based on the Taguchi Method, The International Journal of Advanced Manufacturing Technology, 38, 896–903.
• Kaya, B., 2009. An Online Tool Condition Monitoring System Development for Milling Processes Using Sensor and Decision Integration, PhD Thesis, Kocaeli University, Kocaeli.
• Kayabaşı, O., Çakmak, H., (2019). Design Methodology Of Plastic Injection Process Using Approximate Solution Techniques, Journal of Engineering Sciences and Design, 7(3), 627-638.
• Lin Y.C, Wu K.D., Shih W.C., Hsu P.K., Hung J.P., 2020. Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network, Appl. Sci., 10, 3941; doi:10.3390/app10113941.
• Mavi, A., Korkut, İ., 2010. Modeling With Regression Analysis of the Effect of Cutting Parameters on Cutting Forces and Surface Roughness in Machining Vermicular Graphite Cast Iron, Journal of Polytechnic, 13, 281-286.
• Meral, G., Dilipak, H., Sarıkaya, M., 2011. Modeling with Regression Methods of the Thrust Forces and The Surface Roughness in The Drilling of AISI 1050 Materials, Turkish Science Research Foundation, 4, 31-41.
• Nalbant, M., Gökkaya, H., Sur, G., 2007. Application of Taguchi Method in the Optimization of Cutting Parameters for Surface Roughness in Turning, Materials & Design, 28 (4), 1379-1385.
• Nas, E., Samtaş, G., Demir, H., 2012. Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling, Pamukkale University Journal of Engineering Sciences, 18, 47-59.
• Ranganathan, S., Senthilvelan, T., Sriram, G., 2009. Mathematical Modeling of Process Parameters on Hard Turning Of AISI 316 SS by WC Insert, Journal of Scientific & Industrial Research, 68, 592-596.
• Revankar, Goutam D., Shetty, Raviraj, Shetty, Rao, Shrikantha S., Gaitonde Vinayak N., 2014. Selection of Optimal Process Parameters in Ball Burnishing of Titanium Alloy, An International Journal Machining Science and Technology, 18, 464-483.
• Simunovic, K., Simunovic G., Saric, T., 2013. Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization, Measurement Science Review, 13, 265-272.
• Taşdemir, Ş., 2011. The Comparative Study to Determine Surface Roughness with Artificial Neural Network and Regression, Selcuk University Journal of Technical-Online, 10, 215-226.
• Toprak, I.B., Caglar, M.F., Colak, O., Kıran, K., Bayhan, M., 2012. Optimization of Surface Roughness by Using Taguchi Method in Milling of Ti-6Al-4 V Super- Alloy at High- Pressure Cooling Conditions, SDU International Journal of Technological Sciences, 4(2), 30-39.
• Vikram, K.A., Ratnam, Ch., 2012. Empirical Model for Surface Roughness in Hard Turning Based on Analysis of Machining Parameters and Hardness Values of Various Engineering Materials, International Journal of Engineering Research and Applications, 2, 3091-3097.
• Xiao M., Shen X., Ma Y., Yang F., Gao N., Wei W., Wu D., 2018. Prediction of Surface Roughness and Optimization of Cutting Parameters of Stainless Steel Turning Based on RSM, Mathematical Problems in Engineering, vol. 2018, Article ID 9051084, 15 pages. https://doi.org/10.1155/2018/9051084.
• Yang, Y.K., Chuang, M.T., Lin, S.S., 2009. Optimization of Dry Machining Parameters for High-Purity Graphite in End Milling Process Via Design of Experiments Methods, Journal of Materials Processing Technology, 209, 4395-4400.
• Zhang, J.Z.,Chen, J.C., Kirby, E.D., 2007. Surface Roughness Optimization in an End-Milling Operation Using the Taguchi Design Method, Journal of Materials Processing Technology, 184, 233-239.