Otomat çeliğinin tornalama işleminde iş parçası çapı ve çıkıntı uzunluğunun yüzey pürüzlülüğü üzerindeki etkilerinin deneysel incelenmesi ve yüzey pürüzlülüğünün ANFIS ile modellenmesi

Yıl 2019, , 675 - 686, 23.05.2019

Mustafa Dere İ. Hüseyin Filiz

https://doi.org/10.17341/gazimmfd.416524

Cited By: 2

Öz

Torna tezgâhlarında imal edilen parçalarda istenilen yüzey kalitesini elde etmek için, uygun kesme parametrelerinin seçilmesi (kesme hızı, ilerleme hızı ve kesme derinliği) oldukça önemlidir. Bu çalışmanın amacı, kesme parametrelerine ilave olarak, yüzey pürüzlüğünün iş parçası boyutuna (çap ve çıkıntı uzunluğu) bağlı olarak nasıl etkilendiğini deneysel olarak ortaya koymak ve verilen parametrelere göre yüzey pürüzlülüğünü tahmin eden bir model ortaya koymaktır. İlerleme hızı, kesme hızı, kesme derinliği ve farklı çıkıntı uzunlukları değişken parametreler olarak alınıp iki farklı çapta iş parçası CNC torna tezgâhında tornalanmıştır. Deneyler tam faktöriyel deney tasarımı yöntemine göre tasarlanmış olup, 4 farklı parametre 3 seviye için, iki farklı çapta (her çap için) 3⁴ =81 deney yapılmıştır. Deneylerden elde edilen sonuçlar kullanılarak, Adaptif Sinir Ağına Dayalı Bulanık Çıkarım Sistemi (ANFIS) ile yüzey pürüzlülüğünü tahmin eden modeller geliştirilmiş ve her çap için en iyi model seçilmiştir. Oluşturulan modeller endüstride talaşlı imalat işlemlerinde istenilen yüzey kalitesini elde etmede işlem parametrelerinin doğru ve uygun seçimi için bir yol gösterici olacaktır.

Anahtar Kelimeler

Yüzey pürüzlülüğü, kesme parametreleri, iş parçası çap ve çıkıntısı, ANFIS

Kaynakça

• Albrecht A.B., How to secure desired surface finish in turning operations, Journal of Advanced Manufacturing Technology, 6,133-136, 1956.
• Olsen K.V.,Surface Roughness on Turned Steel Components and the Relevant Mathematical Analysis. Production Engineer, 593-606,1963.
• Takeyama M., Ono T.,Study on roughness of turned surface. Bulletin of the Japan Society of Precision Engineering, 1,4,274-279,1967.
• Nassirpour F.,Wu S.M.,Statistical evaluation of surface finish and its relationship to cutting parameters in turning. International Journal of Machine Tool Design and Research,17,4, 197-208,1977.
• Choudhury I.,El-Baradie M., Surface roughness prediction in the turning of high-strength steel by factorial design of experiments. Journal of Materials Processing Technology,67,55-61,1997.
• Suresh P.V.S., P.Venkateswara Rao, S.G.Deshmukh, A genetic algorithmic approach for optimization of surface roughness prediction model. International Journal of Machine Tools and Manufacture, 42, 675-680,2002.
• Sahin Y.,Motorcu A.R, Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. International Journal of Refractory Metals and Hard Materials,26,84-90,2008.
• Sundaram R.M.,Lambert B.K., Mathematical models to predict surface finish in fine turning of steel. Part II. International Journal of Production Research,19,547-556,1981.
• Özel T.,Karpat Y., Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45,467-479,2005.
• Özel T., Karpat Y., Figueira L.,Davim J.P., Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. Journal of Materials Processing Technology,189,192-198,2007.
• Izelu, C.O., Eze,S.C., Oreko B.U., Edward B.A., Effect of depth of cut, Cutting speed and Work-piece Overhang on Induced Vibration and Surface Roughness in the Turning of 41Cr4 Alloy steel. International Journal of Emerging Technology and Advanced Engineering, 4,1, 1-5,2014.
• Cooper J.,DeRuntz B., The relationship between the workpiece extension length/diameter ratio and surface roughness in turning applications. Journal of Industrial Technology, 23, 2, 1-7,2007.
• Risbood K.,Dixit U., Sahasrabudhe A., Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology,132,203-214,2003.
• Swaroop S., Bhosale V.V., Gılke N.R., Surface roughness predıctıon in CNC Turnıng based on motor current usıng ANFIS. International Journal of Mechanical And Production Engineering, ISSN: 2320-2092. 4,9,2016.
• Karayel D., Prediction and control of surface roughness in CNC lathe using artificial neural network. Journal of materials processing technology, 209,3125–3137,2009.
• Asilturk İ., Çunkaş M., Modelling and prediction of surface roughness in turning process using artificial neural network and multiple regression method. Expert systems with applications, 38.5, 5826-5832,2011.
• Kharola, A., Development of an hybrid neuo-fuzzy controllerfor surface roughness prediction in of mild steel during turning. Internation journal of recent advances in mechanical engineering (IJMECH),4,3, 73-79,2015.
• Cica D.,Sredanovic.M., Modelling of tool life and surface roughness in hard turning using soft computing techniques: a comparative study. Int. J. Materials and Product Technology, Vol. 50, No. 1, 49-64,2015.
• R. Anil Raj, M. Dev Anand, K. Leo Dev Wins and A. S. Varadarajan. ANFIS based Model for Surface Roughness Prediction for Hard Turning with Minimal Cutting Fluid Application. Indian Journal of Science and Technology, Vol 9(13), DOI: 10.17485/ijst/2016/v9i13/90562,2016.
• Kumar R.,Chauhan S.,Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al hybrid composites by using response surface methodology (RMS) and artificial neural networking (ANN),Measurment 65,166-180,2015.
• Biswajit Das,Susmita Roy,R.N.Rai,S.C.Saha, studies on effect of cutting parameters on surface roughness of Al-Cu-TiC MMCs:An Artificial Neural Network Approach.Procedia computer Science, 45,745-752,2015.
• Manivel D., Gandhinathan R., Optimization of surface roughness and tool wear in hard turning of
• austempered ductile iron (grade 3) using Taguchi method. Measurement, 93,108–116,2016.
• Sujan Debnath , Moola Mohan Reddy, Qua Sok Yi, Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process usingTaguchi method. Measurement 78 (2016) 111–119,2016.
• Tomov,M.,Kuzinovski,M.,Cichose,P., Development of mathematical models for surface roughness parameter prediction in turning depending on the process condition. International Journal of Mechanical Sciences 113,120–132,2016.
• Karabulut A., Silindirik tornalama sırasında meydana gelen çap hatalarının deneysel olarak araştırılması,
• Journal of The Faculty of Engineering and Architecture of Gazi University, 25,2, 257-265, 2010.
• Pul M., Çalın R.,Çıtak R.,Şeker U., Al Matrisli MgO takviyeli kompozit malzemelerin tornalamasında yüzey pürüzlülüğünün incelenmesi, Journal of The Faculty of Engineering and Architecture of Gazi University, 26,1, 81-88, 2011.
• Carou D.,Rubio E.M., Lauro C.H., Davimarabulut J.P., Experimental investigation on surface finish during intermittent turning of UNS M11917 magnesium alloy under dry and near dry machining conditions, Measurement, 56, 136–154,2014.
• Jang, J.S.R., Sun, C.T., Mizutani, E., Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Inc.,1997.
• Kim, J., Kasabov, N., ANFIS: Adaptive Neurofuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks,1301-1319,1999.
• Yücel, A., Güneri, A.F., Application of Adaptive Neuro Fuzzy Inference System to Supplier Selection Problem. Journal of Engineering and Natural Sciences, 224-234,2010.
• Subaşı, S., Şahin, İ., Çomak, B., Tahribatsız Test Sonuçları Kullanılarak Uçucu Kül İkameli Betonlarda Basınç Dayanımının ANFIS ile Tahmini. International Journal of Technologic Sciences pp. 9-16, 2010.
• Jang, J.S.R., ANFIS: Adaptive-networkbased fuzzy inference systems. IEEE Trans. Syst. Man Cybern, 665-685, 1993.
• Ö. Demirel, ANFIS ve ARMA Modeller İle Elektrik Enerjisi Yük Tahmini. Marmara Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, 2009.
• K.P. Singh, Basant, A., Malik, A., Jain, G. Artificial neural network modeling of the river water quality—A case study. Ecological Modelling, 2206,888-895, 2009.
• E. Doğan, A. Ateş, E.C. Yılmaz, B. Eren, Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand. Environmental Progress, 27,4,439-446, 2008.