Improved model to predict machined surface roughness based on the cutting vibrations signal during hard turning

• Autorzy: Delijaicov S. , Leonardi F. , Bordinassi E. , Batalha G.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The objective was to study the influence of cutting vibrations in hard turning of AISI 1045 steel. Design/methodology/approach: A design of experiments using a complete factorial was used in the experiments. The specimens were tempered and quenched with 53 HRC. A piezoelectric dynamometer for turning with an acquisition data system was used in the measurements. Findings: The results showed excellent correlation between the model and results and showed that the frequency amplitudes increase the model reliability by 5%. Research limitations/implications: The instrumentation of machine and its correlation with the amplitudes of frequencies from data system acquisition could personalize the models for each experiment on the machines. Originality/value: The paper uses a commercial piece and provides important information for the improvements in the roughness of hardened steel, which is an important factor for the components surface integrity.

Słowa kluczowe

EN

machining; turning; vibration; surface roughness

PL

obróbka skrawaniem; toczenie; wibracje; chropowatość powierzchni

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2010
• Tom: Vol. 45, nr 2
• Strony: 102--107
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Delijaicov S.

autor

Leonardi F.

autor

Bordinassi E.

autor

Batalha G.

• Centro Universitário FEI, Av. Humberto de Alencar Castelo Branco, 3942 - S. Bernardo do Campo, SP - Brazil,

Bibliografia

• [1] H. Jiang, X. Long, G. Meng, Study of the correlation between surface generation and cutting vibrations in peripheral milling, Journal of Materials Processing Technology 208 (2008) 229-238.
• [2] M.C. Cakir, C. Ensarioglu, I. Demirayak, Mathematical modelling of surface roughness for evaluating the effects of cutting parameters and coating material, Journal of Materials Processing Technology 209/1 (2009) 102-109.
• [3] Y. Altintas, Manufacturing automation, metal cutting mechanics, machine tool vibrations and CNC design, Cambridge University Press, 2000.
• [4] L. Huang, J. Chen, A multiple regression model to predict in-process surface roughness in turning operation via accelerometer, Journal of Industrial Technology 17 (2001) 2-8.
• [5] M. Arizmendi, F.J. Campa, J. Fernández, L.N. Lópes de Lacalle, A. Gil, E. Bilbao, F. Veiga, A. Lamikiz, Model for surface topography prediction in peripheral milling considering tool vibration, CIRP Annals - Manufacturing Technology 58/1 (2009) 93-96
• [6] J. Rech, G. Kermouche, W. Grzesik, C. Garcia-Rosales, A. Khellouki, V. Garcia-Navas, Characterization and modelling of the residual stresses induced by belt finishing on a AISI52100 hardened steel, Journal of Materials Processing Technology 208 (2008) 187-195.
• [7] K. Bouacha, M.A. Yallese, T. Mabrouki, J.F. Rigal, Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN toll, International Journal of Refractory Metals and Hard Materials 28/3 (2009) 349-361.
• [8] A.M. Zain, H. Haron, S. Sharif, Prediction of surface roughness in the end milling using artificial neural network, Expert Systems with Applications 37 (2010) 1755-1768.
• [9] D. Karayel, Prediction and control of surface roughness in CNC lathe using artificial neural network, Journal of Materials Processing Technology 209 (2009) 3125-3137.
• [10] J. Valicek, J. Mullerova, S. Hloch, Interpretation of the rough roughness measurement spectra of the surface profiles, Machines Technologies Materials (2008) 22-24.
• [11] P.G. Bernardos, G.C. Vosniakos, Prediction surface roughness in machining: a review, International Journal of Machine Tools and Manufacture 43 (2003) 833-844.
• [12] Ch. Lu, Study on prediction of surface quality in machining process, Journal of Materials Processing Technology 205/1- 3 (2008) 439-450.