Real Time Analysis of Material Removal Rate and Surface Roughness for Turning of Al-6061 using ANN and GA

Authors

  • Abhishek Jha Under Graduate Student, Department of Mechanical Engineering, Delhi Technological University, New Delhi, India
  • Baibhav Kumar Under Graduate Student, Department of Mechanical Engineering, Delhi Technological University, New Delhi, India
  • Ashok Kumar Madan Professor, Department of Mechanical Engineering, Delhi Technological University, New Delhi, India

Keywords:

Artificial Neural Network (ANN), Material Removal Rate (MRR), Genetic Algorithm (GA), Surface roughness, Depth of cut, Feed rate, Regression analysis, ANOVA

Abstract

The paper shows and includes targeted supervision of optimizing machining parameters. Material Removal Rate and surface roughness being integral to machining efficacy of any workpiece, simulation-based modeling helps in failure mitigation. The potential of ANN-GA mathematical approach for prediction and optimization of MRR and surface roughness of AL 6061, an analysis based statistical study has been discussed. The computational model between the desired output and the inputs have been configured using Multiple Regression- Genetic Algorithm and Artificial Neural Network (ANN) methods. The closeness in predicted and optimized data sets were mapped using integrational ANN with GA to interpolate efficacy in optimality.

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Published

31-03-2022

Issue

Vol. 5 No. 3 (2022)

Section

Articles

License

Copyright (c) 2022 Abhishek Jha, Baibhav Kumar, Ashok Kumar Madan

Creative Commons License

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

How to Cite

[1]
A. Jha, B. Kumar, and A. K. Madan, “Real Time Analysis of Material Removal Rate and Surface Roughness for Turning of Al-6061 using ANN and GA”, IJRESM, vol. 5, no. 3, pp. 145–150, Mar. 2022, Accessed: Nov. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/1882