“An Artificial Neural Network Approach to Investigate Surface Roughness of Al 8011 and Nano Zro2 Composites in CNC Turning Process”

Authors

  • Thippeswamy J C Research Scholar, Department of Mechanical Engineering, Shridevi Institute of Engineering & Technology, Tumakuru, Karnataka.
  • Sathisha N Professor and Head,Department of Mechanical Engineering, Yenepoya Institute of Technology, Moodabidri, Karnataka.
  • Narendra Viswanath Professor and Head of the Research Centre, Department of Mechanical Engineering, Shridevi Institute of Engineering & Technology, Tumakuru, Karnataka.

Keywords:

Al 8011,Nano ZrO2,Artificial Neural Network, Surface Roughness

Abstract

In order to maximize surface roughness (Ra), this Research looks at converting composites made of nano ZrO2 and aluminum 8011 alloys. Machining parameters of speed (500, 1000, 1500 rev/min), feed (0.1, 0.2, 0.3 mm/rev), and depth of cut (0.5, 1.0, 1.5) were used in L27 Taguchi's orthogonal tests.A mathematical Surface Roughness (Ra) model has been established for the CNC turning process utilizing an Artificial Neural Network (ANN) model. When utilizing an artificial neural network model, a deviation of 1.89% errors is observed for Ra. The variance of the ANN-predicted and experimental findings is within the allowable range. The ANN model showed better forecasting ability.

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Published

2024-09-22

How to Cite

Thippeswamy J C, Sathisha N, & Narendra Viswanath. (2024). “An Artificial Neural Network Approach to Investigate Surface Roughness of Al 8011 and Nano Zro2 Composites in CNC Turning Process”. Journal of Computational Analysis and Applications (JoCAAA), 33(07), 730–739. Retrieved from http://eudoxuspress.com/index.php/pub/article/view/1134

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