Journal of Production Engineering

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Vol. 21 No. 2 (2018)
Original Research Article

Artificial inteligence approache to modeling of cutting force and tool wear relationships during dry machining

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  • Pavel Kovač,
  • Boris Savkovic,
  • Dragan Rodić,
  • Ildiko Mankova
Pavel Kovač
University of Novi Sad, Faculty of Technical Sciences, Departman for Production Engineering, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Boris Savkovic
University of Novi Sad, Faculty of Technical Sciences, Departman for Production Engineering, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Dragan Rodić
University of Novi Sad, Faculty of Technical Sciences, Departman for Production Engineering, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Ildiko Mankova
Technical University of Košice, Faculty of Mechanical Engineering, Deptartment of Manufacturing Technology and Materials, Mäsiarska 74, 040 01 Košice
DOI: https://doi.org/10.24867/JPE-2018-02-013

Published 2018-12-30

abstract views: 15 // FULL TEXT ARTICLE (PDF): 15


Keywords

  • cutting force,
  • tool wear,
  • experimental dry study machining,
  • neural network,
  • regression analyse

How to Cite

Kovač, P., Savkovic, B., Rodić, D., & Mankova, I. (2018). Artificial inteligence approache to modeling of cutting force and tool wear relationships during dry machining. Journal of Production Engineering, 21(2), 13–18. https://doi.org/10.24867/JPE-2018-02-013

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Abstract

In the paper numerical and experimental study for different cutting conditions according planning of experiment was carried out. Contribution was made during dry face milling process what contributes to sustainability of manufacturing processes. Cutting force components and parameters of tool wear versus time were pointed out. It was observed that cutting force components increase with time and/or tool wear. The relationships for cutting force components versus cutting depth, feed and tool wear parameters were expressed by regression analyse and artificial neural network.

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FULL TEXT ARTICLE (PDF)