Modelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches

Authors

  • B. Ben Fathallah Mechanical, Material and Process Laboratory (LR11ES19), ENSIT - University of Tunis, 5 AV Taha Hussein Montfleury, Tunis Tunisia. Phone: +21658495479; Fax: +21671872729
  • R. Saidi Applied Mechanics and Engineering Laboratory (LR-11-ES19), University of Tunis El Manar, ENIT, BP 37, Le Belvédère, 1002 Tunis, Tunisia
  • S. Belhadi Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, P.O. Box 401, 24000 Guelma, Algeria
  • M.A. Yallese Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, P.O. Box 401, 24000 Guelma, Algeria
  • T. Mabrouki ENIT, University of Tunis El Manar, BP 37, Le Belvédère, 1002 Tunis, Tunisia

DOI:

https://doi.org/10.15282/jmes.15.4.2021.07.0673

Keywords:

Stellite 6, turning, RSM, ANOVA, ANN, SVM

Abstract

The present research work proposes an experimental investigation helping to comprehend fundamental impacts of operating conditions during the cutting of cobalt alloys (Stellite 6). Thus, an experimental design was adopted to allow to build predicted mathematical models for the outputs, which are the average peak-to-valley profile roughness (Rz) and the tangential cutting force (Ft). Artificial neural network (ANN), support vector machine (SVM) and response surface methodology (RSM) were exploited to model the pre-cited outputs according to operation parameters. As a result, it has been highlighted that both feed rate and cutting depth, considerably, affect tangential cutting force evolution. Moreover, results show that both the insert feed rate and nose radius, are higher. This means the average peak-to-valley profile roughness is higher. In order to put out the effect of operating parameters on cutting outputs, Analysis of variance (ANOVA) method has been employed. This has allowed the detection of significant cutting conditions affecting average peak-to-valley profile roughness and tangential cutting force. In fact, to highlight the performance of adopted mathematical approaches, a comparison between RSM, ANN, and SVM has been also established in this study.

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Published

2021-12-15

How to Cite

Ben Fathallah, B., R. Saidi, S. Belhadi, M.A. Yallese, & T. Mabrouki. (2021). Modelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches. Journal of Mechanical Engineering and Sciences, 15(4), 8540–8554. https://doi.org/10.15282/jmes.15.4.2021.07.0673