Data-driven simulations of flank wear of coated cutting tools in hard turning

Authors

  • Ahmet Cakan Mersin University
  • Fatih Evrendilek Abant Izzet Baysal University
  • Vedat Ozkaner Mustafa Kemal University

DOI:

https://doi.org/10.5755/j01.mech.21.6.12199

Keywords:

carbide tools, online monitoring, data-driven modeling, finish turning

Abstract

Insurance of surface quality and dimensional tolerances in finish turning necessitates the development of accurate predictive models. This study aimed at modeling flank wear of multilayer-coated carbide inserts in finish dry hard turning of AISI 4340 and AISI 52100 hardened steels based on 28 artificial neural networks (ANNs) and the best-fit multiple non-linear regression (MNLR) model. Online-monitored flank wear of multilayer-coated carbide inserts was modeled as a function of the three cutting speeds of 70, 98 and 142 m min-1, and the two workpieces under the constant feed rate and cutting depth of 0.027 mm min-1 and 0.2 mm, respectively. Out of the 28 ANNs, 18 ANNs appeared to be capable of better predictions for tool flank wear than the best-fit MNLR model. Probabilistic neural network (PNN) outperformed all the remaining models based on all the training, cross-validation and testing dataset-related metrics.

 

DOI: http://dx.doi.org/10.5755/j01.mech.21.6.12199

Author Biographies

Ahmet Cakan, Mersin University

Associate Professor of Mechanical Engineering of Mersin University

Fatih Evrendilek, Abant Izzet Baysal University

Professor of Environmental Engineering of Abant Izzet Baysal University

Vedat Ozkaner, Mustafa Kemal University

Associate Professor of Electrical and Electronical Engineering of Mustafa Kemal University

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Published

2015-12-22

Issue

Section

MECHANICAL TECHNOLOGIES