PREDICTION OF FLANK WEAR IN DRILLING USING DIFFERENTIAL EVOLUTION TRAINED NEURAL NETWORKS

Authors

  • Pandu R Vundavilli Mechanical Engineering, DVR & Dr. HS MIC College of Technology, Kanchikacherla, Andra Pradesh-521180, India.
  • Jagdeesh Anne Mechanical Engineering, Rungta College of Engineering & Technology, Bhilai, Chattishgarh -490024, India.
  • Subba Rao C V Mechanical Engineering, QIS College of Engineering & Technology, Ongole, Andra Pradesh-523272, India.

Keywords:

Flank Wear, Drilling, Differential Evolution, Neural Network

Abstract

The rising demand for enhanced performance of manufacturing system has led to new challenges for the development of complex tool condition monitoring techniques. Estimation of tool life generally requires considerable time and relatively expensive. In the present paper, Differential Evolution trained Neural Network (DE-NN) has been developed to predict the flank wear in drilling operation. In DE-NN, the flank wear prediction problem of a drilling operation has been modeled using an NN, whose weights and bias values are optimized offline, using DE. The performance of the developed approach has been compared in terms of their prediction accuracy.

 

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References

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Published

2011-03-01

Issue

Section

Articles

How to Cite

[1]
“PREDICTION OF FLANK WEAR IN DRILLING USING DIFFERENTIAL EVOLUTION TRAINED NEURAL NETWORKS”, JME, vol. 6, no. 1, pp. 024–029, Mar. 2011, Accessed: Dec. 22, 2024. [Online]. Available: https://smenec.org/index.php/1/article/view/439

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