Prédiction de la rugosité de surface par réseaux de neuronnes

Conference paper

FARID BOUKEZZI, RACHID NOUREDDINE, FARID NOUREDDINE
12è edition de Congrès International de Génie Industriel (CIGI 2017), Compiegne, France, 2017 May

Read

Cite

APA Click to copy
BOUKEZZI, F. A. R. I. D., NOUREDDINE, R. A. C. H. I. D., & NOUREDDINE, F. A. R. I. D. (2017). Prédiction de la rugosité de surface par réseaux de neuronnes. In 12è edition de Congrès International de Génie Industriel (CIGI 2017). Compiegne, France.

Chicago/Turabian Click to copy
BOUKEZZI, FARID, RACHID NOUREDDINE, and FARID NOUREDDINE. “Prédiction De La Rugosité De Surface Par Réseaux De Neuronnes.” In 12è Edition De Congrès International De Génie Industriel (CIGI 2017). Compiegne, France, 2017.

MLA Click to copy
BOUKEZZI, F. AR ID, et al. “Prédiction De La Rugosité De Surface Par Réseaux De Neuronnes.” 12è Edition De Congrès International De Génie Industriel (CIGI 2017), 2017.

BibTeX Click to copy

@inproceedings{boukezzi2017a,
  title = {Prédiction de la rugosité de surface par réseaux de neuronnes},
  year = {2017},
  month = may,
  address = {Compiegne, France},
  journal = {12è edition de Congrès International de Génie Industriel (CIGI 2017)},
  author = {BOUKEZZI, FARID and NOUREDDINE, RACHID and NOUREDDINE, FARID},
  month_numeric = {5}
}

Abstract

Surface roughness (Ra) is a very important measurement in machining process and is mostly used as an index to determine the surface finish. This research presents the prediction of surface roughness in turning process using artificial neural network (ANN) model from the following input parameters: cutting speed (𝒗𝒗𝒄𝒄), feed rate (𝒂𝒂) and depth of cut (𝒑𝒑).The (ANN) model shows a good correlation between the predicted and the experimental surface roughness values. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

Keywords

Prediction, surface roughness, ANN, turning, cutting parameters