Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Journal article

Farid Boukezzi, Rachid Noureddine, Ali Benamar, Farid Noureddine
International Journal of Industrial and Systems Engineering, vol. 26, Inderscience Publishers (IEL), 2017, pp. 567--583

DOI: 10.1504/IJISE.2017.085227

Read

Cite

APA Click to copy
Boukezzi, F., Noureddine, R., Benamar, A., & Noureddine, F. (2017). Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network. International Journal of Industrial and Systems Engineering, 26, 567–583. https://doi.org/10.1504/IJISE.2017.085227

Chicago/Turabian Click to copy
Boukezzi, Farid, Rachid Noureddine, Ali Benamar, and Farid Noureddine. “Modelling, Prediction and Analysis of Surface Roughness in Turning Process with Carbide Tool When Cutting Steel C38 Using Artificial Neural Network.” International Journal of Industrial and Systems Engineering 26 (2017): 567–583.

MLA Click to copy
Boukezzi, Farid, et al. “Modelling, Prediction and Analysis of Surface Roughness in Turning Process with Carbide Tool When Cutting Steel C38 Using Artificial Neural Network.” International Journal of Industrial and Systems Engineering, vol. 26, Inderscience Publishers (IEL), 2017, pp. 567–83, doi:10.1504/IJISE.2017.085227.

BibTeX Click to copy

@article{boukezzi2017a,
  title = {Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network},
  year = {2017},
  journal = {International Journal of Industrial and Systems Engineering},
  pages = {567--583},
  publisher = {Inderscience Publishers (IEL)},
  volume = {26},
  doi = {10.1504/IJISE.2017.085227},
  author = {Boukezzi, Farid and Noureddine, Rachid and Benamar, Ali and Noureddine, Farid}
}

Abstract

Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed (v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

Keywords

Prediction, surface roughness, artificial neural network, turning, cutting parameters, artificial neural network, ANN, modelling, simulation

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Journal article

Cite

Abstract

Keywords

Follow this website