Multivariate empirical modeling of interaction effects of machining var-iables on surface roughness in dry hard turning of AISI 4140 steel with coated CBN insert using Taguchi design

Authors

  • Ahmet Cakan
  • Fatih Evrendilek

DOI:

https://doi.org/10.5755/j01.mech.23.5.16223

Keywords:

multivariate analyses, surface quality, data-driven modeling, dry hard turning, Taguchi design

Abstract

The most important measures of surface quality during the machining process are changes in the average surface roughness and maximum peak-to-valley height caused by such machining variables as cutting speed and feed rate. This study quantifies the interaction effects of the machining variables on surface roughness in dry hard turning process of AISI 4140 steel with CBN7015 inserts. The Taguchi experimental design, (multivariate) analysis of variance, and multiple (non-)linear regression models were combined in the quantification and modeling of interaction effects on surface roughness in dry hard turning process. There existed a significant interaction effect between cutting speed and feed rate in determining the behavior of the response variables.

DOI: http://dx.doi.org/10.5755/j01.mech.23.5.16223

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Published

2017-10-25

Issue

Vol. 23 No. 5 (2017)

Section

MECHANICAL TECHNOLOGIES

License

The copyright for the articles in this journal is retained by the author(s) with the first publication right granted to the journal. The journal is licensed under the Creative Commons Attribution License 4.0 (CC BY 4.0).