Modelling of machining parameters to predict the surface roughness Page 134 of 140 using multiple regression and neural network
L. Soni Kurniawan
REFERENCES
[1] Mathew A. Kuttolamadom, Sina Hamzehlouia, and M. Laine Mears ,’ Effect of Machining Feed on Surface Roughness in Cutting 6061 Aluminum ’,SAE International,Clemson,SC,2010.
[2] Rajesh Kumar Bhushan & Sudhir Kumar & S. Das,’ Effect of machining parameters on surface roughnessand tool wear for 7075 Al alloy SiC composite ’, Springer, London, 2010.
[3] Mohammed T. Hayajneh , Montasser S. Tahat , and Joachim Bluhm,’ A Study of the Effects of Machining Parameters on the Surface Roughness in the End- Milling Process ’, JJMIE, vol. 1, no. 1, hh 1-5, Jordan, 2007.
[4] Parth Shah, Falgun Jani,’ Investigation on surface roughness of Aluminium alloy for End milling ’,SJIF, Sieicon, 2017.
[5] Ilhan Asilturk , Harun Akkus,’Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method ’,Measurment, vol. 44,hh. 1687 – 1704 , Konya, Turkey, 2011.
[6] P.G. Benardos, and G.C. Vosniakos,’ Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments’, Robotics and Computer Integrated Manufacturing , vol.18, hh. 343–354, hh.343-354, Athens, Greece,2002.
[7] Tug˘rul O¨ zel, and Yig˘it Karpat,’ Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks’, International Journal of Machine Tools & Manufacture, vol. 45, hh. 467–479, NJ, USA, 2004.
[8] Tugrul O¨ zel , Tsu-Kong Hsu, and Erol Zeren,’ Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel ’, Int J Adv Manuf Technol , vol. 25, hh. 262–269, London, 2004.
[9] Abeesh C. Basheer, Uday A. Dabadea, Suhas S. Joshi, V.V. Bhanuprasad, and V.M. Gadre,’ Modeling of surface roughness in precision machining of metal matrix composites using ANN ’, journal of materials processing technology, vol. 1 9 7, hh. 439–444, Hyderabad, India, 2008.
Modelling of machining parameters to predict the surface roughness Page 135 of 140 using multiple regression and neural network
L. Soni Kurniawan
[10] P.G. Benardos, and G.-C. Vosniakos,’ Predicting surface roughness in machining: a review’, International Journal of Machine Tools & Manufacture ,vol. 43, hh. 833–844, Athens, Greece,2002.
[11] Yusuf Sahin , and A. Riza Motorcu,’ Surface roughness model for machining mild steel with coated carbide tool’, Materials and Design , vol. 26 , hh. 321–
326, Ankara, Turkey,2004.
[12] Mira Febrina1, Faula Arina, and Ratna Ekawati,’ Peramalan Jumlah Permintaan Produksi Menggunakan Metode Jaringan Syaraf Tiruan (JST) Backpropagation’, Jurnal Teknik Industri, vol.1, no.2, hh. 174 – 179, Indonesia.
[13] Y. A. Lesnussa, S. Latuconsina, and E. R. Persulessy,’ Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Memprediksi Prestasi Siswa SMA ’, Jurnal Matematika Integratif vol. 11, no. 2, Indonesia, 2015.
[14] Yue Jiao, Shuting Lei, and Z.J. Pei, E.S. Lee,’ Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations ’, International Journal of Machine Tools & Manufacture, vol. 44 , hh. 1643–1651, USA, 2004.
[15] Ilhan Asiltürk , Mehmet Çunkas,’ Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method ’, Expert and Applications, vol. 38, hh. 5826 – 5832, Turkey, 2011.
[16] B. Sidda Reddy, G. Padmanabhan, and K. Vijay Kumar Reddy,’ Surface Roughness Technique for CNC Turning’, Asian Journal of Scientific Research, vol. 1, no. 3, hh. 256 – 264, India, 2008.
[17] Serope Kalpakjian, and Steven R. Schmid, 2011, Manufacturing Engineering and Technology 6th Edition, Pearson ,London.
[18] Swiss Mechanic, 2005, Model – Lehrgang für Polymechaniker und Polymechanikerinen, Swiss Mechanic, Schweiz.
[19] Kusumadewi, S., 2004, Membangun Jaringan Syaraf Tiruan, Graha Ilmu, Yogyakarta.