Model Development for Prediction of Surface Roughness by using of AI Technique

Authors

  • Mohd. Tauseef  Department of Mechanical Engineering, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India
  • Dheeraj Kumar Verma  Department of Mechanical Engineering, Maharishi University of Information and Technology, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET207636

Keywords:

ANN, FFNN, MSE, MRE, Regression, MATLAB

Abstract

The surface roughness of manufactured product is final results of the turning technique parameters, and an critical characteristics that outline product first-rate, aesthetics etc. It imposes one of the most essential constraints for the choice of machines and slicing parameters in manner planning. In this paper, Artificial Neural Network (ANN) method has been used to develop surface roughness prediction model the use of experimental statistics, wherein Feed Forward Neural Network (FFNN) the usage of Back Propagation set of rules and Levenberg-Marquardt education function has been used. The work has been done using Neural etwork Toolbox in MATLAB. The overall performance of the version has been assessed based totally on Regression analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). A three-2-1 model with two neurons in the hidden layer turned into discovered to be the excellent developed model, having universal regression ( R) cost of zero.9923 and pleasant validation overall performance MSE value of 0.00913. The ANN model confirmed incredible consequences for forecasting

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-12-30

Issue

Volume 7, Issue 6, November-December-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mohd. Tauseef, Dheeraj Kumar Verma "Model Development for Prediction of Surface Roughness by using of AI Technique" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 6, pp.286-292, November-December-2020. Available at doi : https://doi.org/10.32628/IJSRSET207636