32
Views
1
CrossRef citations to date
0
Altmetric
technical paper

Investigation of cutting parameters of surface roughness for brass using artificial neural networks in computer numerical control turning

, &
Pages 35-45 | Received 30 Nov 2010, Accepted 12 Jan 2011, Published online: 22 Sep 2015

References

  • Al-Ahmari, A. M. A. 2007, “Predictive machinability models for a selected hard material in turning operations”, Journal of Materials Processing Technology, Vol. 190, pp. 305–311.
  • Azouzi, R. & Guillot, M. 1997, “On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion”, International Journal of Machine tool and Manufacture, Vol. 37, No. 9, pp. 1201–1217.
  • Bajic, D., Lela, B. & Zivkovic, D. 2008, Modeling of machined surface roughness and optimization of cutting parameters in face milling, ISSN 0543–5846.
  • Banerjee, A., Bordatchev, E. V. & Choudhury, S. K. 2009, “On-line monitoring of surface roughness in turning operations with opto-electrical transducer”, International Journal of Manufacturing Research, Vol. 4, No. 1, pp. 57–73.
  • Cus, F. & Balic, J. 2003, “Optimization of cutting process by GA approach”, Robotics and Computer Integrated Manufacturing, Vol. 19, pp. 113-121.
  • Feng, C. X. & Hu, Z. J. 2001, “A comparative study of the ideal and actual surface roughness in finish turning”.
  • Groover, M. 1996, Fundamentals of Modern Manufacturing, Prentice Hall, Upper Saddle River, NJ (now published by John Wiley & Sons, New York).
  • Hossain, M. I., Amin, A. K. M. & Patwari, A. U. 2008, “Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy”, International Conference on Computer and Communication Engineering, ICCCE 2008, No. 13-15, pp. 1321-1324.
  • Huang, L. & Chen, J. C. 2001, “A Multiple Regression Model to Predict In-process Surface Roughness in Turning Operation Via Accelerometer”, Journal of Industrial Technology, Vol. 17, No. 2, pp. 1–8.
  • Mike, S. L., Chen, J. C. & Li, M. 1998, “Surface roughness prediction for CNC end milling, materials and processes quality control manufacturing”, Journal of Industrial Technology, Vol. 15, No. 1, pp. 2–6.
  • Nalbant, M., Gokkaya, H. & Toktas, I. 2007, “Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning”, Modelling and Simulation in Engineering, Hindawi Publishing Corp, New York, NY, United States, Vol. 3, pp. 2.
  • Oktem, H., Erzurumlu, T. & Kurtaran, H. 2005, “Application of response surface methodology in the optimization of cutting conditions for surface roughness”, Journal of Materials Processing Technology, Vol. 170, pp. 11–16.
  • Srikanth, T. & Kamala, V. 2008, “A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning”, IJCSNS International Journal of Computer Science and Network Security, Vol. 8, No. 6, pp. 189-193.
  • Suresh, P. V. S., Venkateswara, R. P. & Deshmukh, S. G. 2002, “A genetic algorithmic approach for optimization of surface roughness prediction model”, International Journal of Machine Tools & Manufacture, Vol. 42, pp. 675–680.
  • Tasdemir, S., Neseli, S., Saritas, I. & Yaldiz, S. 2008, “Prediction of surface roughness using artificial neural network in lathe”, International Conference on Computer Systems and Technologies, CompSysTech’08.
  • Villaseñor, D., Morales, R., Rodríguez, M. C. & Alique, J. R. 2006, “Neural networks and statistical based models for surface roughness prediction”, Proceedings of the 25 th IASTED International Conference on Modeling, Identification and Control, International Association Of Science and Technology for Development, pp. 326–331.
  • Zuperl, U. & Cus, F. 2003, “Optimization of cutting conditions during cutting by using neural networks”, Robotics and Computer Integrated Manufacturing, Vol. 19, pp. 189-199.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.