Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Yıl 2013, Cilt: 1 Sayı: 2, 11 - 20, 01.08.2013

Sıtkı Akıncıoğlu Faruk Mendi Adem Çiçek Gülşah Akıncıoğlu

https://doi.org/10.5505/apjes.2013.87597

Öz

In this study, the effects of cutting speed, feed rate and different types of coating materials on thrust force and hole diameter were investigated in drilling of AISI D2 cold work tool steel. In addition, the thrust forces and hole diameters were predicted by artificial neural networks (ANN) using experimental data. Uncoated, TiN, TiAlN monolayer and TiAlN/TiN multi-layer coated cemented carbide drills with diameter of 5 mm were used in drilling experiments. The holes were drilled at different combinations of four cutting speeds (50, 55, 60, 65 m/min), two feed rates (0.063 and 0.08 mm/rev), and fixed depth of cut (7 mm). Experimental results showed that the lowest thrust forces and hole diameters were obtained with TiAlN/TiN multi-layer coated drills. After ANN training, it was found that the R2 values are very close to 1 for both training and test sets. RMSE values are smaller than 0.03, and mean error values are smaller than 5% for the test set. This case shows that ANN is a powerful method for prediction of thrust forces and hole diameters.

Anahtar Kelimeler

Coatings, Thrust force, Hole diameter, Artificial neural network

Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools

Öz

.

Anahtar Kelimeler

xx

Kaynakça

• Duran A, Acır A. HSS Torna Kalemindeki Talaş Açısının Kesme Kuvvetlerine Etkisi. Politeknik Dergisi 2004; 211-215.
• Çakır MC, Modern Talaşlı İmalat Yöntemleri. Vipaş A.Ş: Bursa;2000, 350-390.
• Basavarajappa S, Chandramohan G, Davim JP. Some studies on drilling of hybrid metal matrix composites based on Taguchi techniques. j mater process tech 2008; 196: 332–338
• Guu YH, Hocheng H. Improvement of fatigue life of electrical discharge machined AISI D2 tool steel by TiN coating. matscieng a-struct2001; 318: 155– 162.
• Lin TR. Cutting behavior of a TiN-coated carbide drill with curved cutting edges during the high-speed machining of stainless steel. j mater process tech 2002; 127: 8-16.
• Chou CWC, Liu XD. Study on the various coated twist drills for stainless steels drilling. j mater process tech 2000; 99: 226-230.
• Sharif S, Rahim EA. Performance of coated and uncoated-carbide tools when drilling titanium alloy- Ti–6Al4V. j mater process tech 2007;185: 72-76.
• Smith IJ, Gillibrand D, Brooks JS, Miinz D, Harvey S, Goodwin R. Dry cutting performance of HSS twist drills coated with improved TiAlN. surf coat tech 1997; 90: 164-171.
• Grips VKW, Barshilia HC, Selvi VE, Kalavati, Rajam KS. Electro chemical behavior of single layer CrN, TiN, TiAlN coatings and nano layered TiAlN/CrN multilayer coatings prepared by reactive direct current magnetron sputtering. Thin Solid Films 2006; 514: 204–211.
• Aihuaa L, Jianxina D, Haibinga C, Yangyanga C, Juna Z. Friction and wear properties of TiN, TiAlN, AlTiN and CrAlN PVD nitride coatings. int j refract met h 2012; 31: 82–88.
• Hsieha JH, Liang C, Yu CH, Wu W. Deposition and characterization of TiAlN and multi-layered TiAlN/TiN coatings using unbalanced magnetron sputtering. surf coat tech 1998; 108–109 132–137.
• Zuperl U, Cus F. Optimization of cutting conditions during cutting by using neural networks. robotcim-int manuf2003; 19: 189–199.
• Çay Y, Çiçek A, Kara F, Sagiroglu S. Prediction of engine performance for an alternative fuel using artificial neural network. applthermeng2012; 37: 217- 225.
• Sanjay C, Neema ML, Chin CW. Modeling of tool wear in drilling by statistical analysis and artificial neural network. j mater process tech 2005;170: 494–500.
• Wong SV, Hamouda AMS. Machinability data representation with artificial neural network. j mater process tech 2003; 138: 538–544.
• Aykut Ş, Gölcü M, Semiz S, Ergür HS. Modeling of cutting forces as function of cutting parameters for face milling of stellite 6 using an artificial neural network. J Mater Process Technol 2007; 190: 199–203.
• Benardos PG, Vosniakos GC. Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. robotcim-int manuf 2002; 18: 343–354.
• El-Mounayri H. Kishawy H. Briceno J. Optimization of CNC ball end milling: a neural network-based model. J mater process tech 2005; 166: 50–62.
• Michael F, Kahles JF, Koster WP. Surface finish and surface integrity. American Society for Metals1989; 3: 468-475.
• Kıvak T. INCONEL 718’in Delinebilirliğinin Araştırılması. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü Ankara 2007; 21-33.
• Çiçek A, Kıvak T, Samtaş G, Çay Y. Modelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis. J mecheng2012; 587-8: 492- 498.
• Çay Y, Korkmaz İ, Çiçek A, Kara F. Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy 2013; 50: 177-186.
• Akıncıoğlu S, Mendi F, Çiçek A, Akıncıoğlu G. ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel. Int J Adv Manuf Technol DOI 10.1007/s00170-012-4719-6.
• Davim JP, Baptista, A.M. Cutting force, tool wear and surface finishing drilling metal matrix composites. ProcInst Mech Eng 2001; 215: 177–183. [25] Morin E, Masounave J, Laufer EE. Effect of wear on cutting forces in the drilling of metal matrix composites. Wear 1995; 184: 11–16.
• Songmene AV, Balazinzki M. Machinibility of graphitic metal matrix composites as a function of reinforcing particles. CIRP 1999; 48 (1): 77–80.