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Prediction of Forces Components During the Turning Process of Stellite 6 Material Based on Artificial Neural Networks

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Design and Modeling of Mechanical Systems - IV (CMSM 2019)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In this study, artificial neural networks (ANNs) were used to study the effects of machinability on cutting parameters during turning of the cobalt alloy (Stellite 6). Cutting forces with three axes (Fx, Fy and Fz) were predicted by changing the tool tip radius (r), cutting speed (Vc), feed rate (f) and cutting depth (ap) with conventional lubrication. Experimental studies were conducted to obtain training and test data and a feed-forward back-propagation algorithm was used in the networks. The main test parameters are the tool tip radius (r, mm), cutting speed (Vc, m/min), feed rate (f, mm/rev), cutting depth (ap, mm) and cutting forces (Fx, Fy and Fz, N). r, Vc, f and ap were used as input data while Fx, Fy and Fz were used as output data. The mean percentage values of root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD) for Fx, Fy and Fz using the proposed models were obtained around 2 and 4.79%, respectively for training and testing. These results show that ANNs can be used to predict the effects of machinability on cutting parameters when cutting Stellite 6 on turning process. The results highlighted the performance of the studied configuration.

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Author information

Authors and Affiliations

  1. Applied Mechanics and Engineering Laboratory (LR-11-ES19), University of Tunis El Manar, ENIT, BP 37, Le Belvédère, 1002, Tunis, Tunisia

    Riadh Saidi & Tarek Mabrouki

  2. Mechanical, Material and Process Laboratory (LR99ES05) ENSIT, University of Tunis, 5 AV Taha Hussein Montfleury, Tunis, Tunisia

    Brahim Ben Fathallah

  3. Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, 401, 24000, Guelma, Algeria

    Salim Belhadi & Mohamed Athmane Yallese

Authors
  1. Riadh Saidi
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  2. Brahim Ben Fathallah
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  3. Tarek Mabrouki
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  4. Salim Belhadi
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  5. Mohamed Athmane Yallese
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Corresponding author

Correspondence to Riadh Saidi .

Editor information

Editors and Affiliations

  1. Preparatory Institute of Engineering Studies of Monastir (IPEIM), Monastir, Tunisia

    Nizar Aifaoui

  2. National Engineering School of Monastir (ENIM), Monastir, Tunisia

    Zouhaier Affi

  3. National School of Engineers of Sfax (ENIS), Sfax, Tunisia

    Mohamed Slim Abbes

  4. National School of Engineers of Sfax (ENIS), Sfax, Tunisia

    Lassad Walha

  5. National School of Engineers of Sfax (ENIS), Sfax, Tunisia

    Mohamed Haddar

  6. National Engineering School of Sousse (ENISO), Sousse, Tunisia

    Lotfi Romdhane

  7. National Engineering School of Monastir (ENIM), Monastir, Tunisia

    Abdelmajid Benamara

  8. National Engineering School of Monastir (ENIM), Monastir, Tunisia

    Mnaouar Chouchane

  9. Department of Mechanical Engineering, National School of Engineers of Sfax (ENIS), Sfax, Tunisia

    Fakher Chaari

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Saidi, R., Fathallah, B.B., Mabrouki, T., Belhadi, S., Yallese, M.A. (2020). Prediction of Forces Components During the Turning Process of Stellite 6 Material Based on Artificial Neural Networks. In: Aifaoui, N., et al. Design and Modeling of Mechanical Systems - IV. CMSM 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-27146-6_43

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  • DOI: https://doi.org/10.1007/978-3-030-27146-6_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27145-9

  • Online ISBN: 978-3-030-27146-6

  • eBook Packages: EngineeringEngineering (R0)

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