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Journal of Intelligent Manufacturing

, Volume 19, Issue 4, pp 473–483 | Cite as

Estimation of cutting forces and surface roughness for hard turning using neural networks

  • Vishal S. Sharma
  • Suresh Dhiman
  • Rakesh Sehgal
  • S. K. Sharma
Article

Abstract

Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experimentation is analyzed and used to construct model using neural networks. The model obtained is then tested with the experimental data and results are indicated.

Keywords

Cutting forces Surface roughness Neural networks 

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References

  1. Alajmi, M. S., & Alfares, F. (2007). Prediction of cutting forces in turning process using De-Neural Networks. Artificial Intelligence and Applications, AIA 2007, 2/12/2007–2/14/2007, Innsbruck, Austria.Google Scholar
  2. Chou, Y.K., Evans, C.J., Barash, M.M.: Experimental investigation on CBN turning of hardened AISI 52100 steel. Journal of Materials Processing Technology 124, 274–283 (2002)CrossRefGoogle Scholar
  3. Dimla, D.E. Sr.: Application of perceptron neural networks to tool state classification in a metal turning operation. Engineering Applications of Artificial Intelligence 12, 471–477 (1999)CrossRefGoogle Scholar
  4. Feng, C.X., Wang, X.: Development of empirical models for surface roughness prediction in finish turning. International Journal of Manufacturing Technology 20, 348–356 (2002)CrossRefGoogle Scholar
  5. Feng, C.-X.J., Yu, Z.-G.(Samuel), Kusiak, A.: Selection and validation of predictive regression and neural network model based on designed experiments. IIE Transactions 38, 13–23 (2006)CrossRefGoogle Scholar
  6. Haci, S., Faruk, U., Yaldiz, S.: Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. International Journal of Machine Tools & Manufacture 46(2), 132–141 (2006)CrossRefGoogle Scholar
  7. Liang, M., Mgwatu, M., Zuo, M.: Integration of cutting parameter selection and tool adjustment decisions for multipass turning. International Journal of Advanced Manufacturing Technology 17(12), 861–869 (2000)CrossRefGoogle Scholar
  8. 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(4–5), 467–479 (2005)CrossRefGoogle Scholar
  9. Rahman, M., Zhou, Q., Hong, G.S.: On-line cutting state recognition in turning using a neural network. The International Journal of Advanced Manufacturing Technology 10(2), 87–92 (1995)CrossRefGoogle Scholar
  10. Sharma, V.S., Sharma, S.K., Sharma, A.K.: Tool wear estimation for turning operations. Journal of Mechanical Engineering 57(3), 141–168 (2006)Google Scholar
  11. Simpson, P. K. (1992). Foundations of neural networks, artificial neural networks. IEEE press.Google Scholar
  12. Singh, D., Rao, P.A.: Surface roughness prediction model for hard turning process. The International Journal of Advanced Manufacturing Technology 32(11–12), 1115–1124 (2007)CrossRefGoogle Scholar
  13. Srinivasa Pai, P., Nagabhushana, T.N., Ramakrishna Rao, P.K.: Flank wear estimation in face milling based on radial basis function neural networks. International Journal Advanced Manufacturing Technology 20(4), 241–247 (2002)CrossRefGoogle Scholar
  14. Thiele, J.D., Melkote, S.N.: Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel. Journal of Materials Processing Technology 94, 216–226 (1999)CrossRefGoogle Scholar
  15. Zuperl, U., Cus, F.: Optimization of cutting conditions during cutting by using neural networks. Robotics and Computer Integrated Manufacturing 19, 189–199 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Vishal S. Sharma
    • 1
  • Suresh Dhiman
    • 2
  • Rakesh Sehgal
    • 2
  • S. K. Sharma
    • 3
  1. 1.Department of Industrial EngineeringNational Institute of TechnologyJalandharIndia
  2. 2.Department of Mechanical EngineeringNational Institute of TechnologyHamirpurIndia
  3. 3.Department of Mechanical EngineeringNational Institute of TechnologyKurukshetraIndia

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