Prediction of the main cutting force in longitudinal turning of AISI D6 tool steel bars by applying full and fractional experimental design

Kyriaki-E Aslani,Nikolaos M Vaxevanidis,Angelos Koutsomichalis,Nektarios Vidakis,Nikolaos A Fountas,Dimitrios E Manolakos,John D Kechagias,G Mansour,D Tzetzis,P Kyratsis

doi:10.1051/matecconf/202031801051

Abstract

This investigation concerns the cutting force magnitude during turning of AISI D6 tool steel using both Taguchi Experimental Design (TED) and full factorial design (FFD). Three main cutting parameters namely spindle speed, feed rate and depth of cut were considered as the cutting parameters, each one having three levels, while the cutting force (Fc) was selected as the machinability process output. The full factorial design of the 27 (33) experiments was splitted in three sub-arrays, each one having 9 experiments. These three sub-arrays were orthogonal and were treated as Taguchi L9 orthogonal arrays. The performance of the FFD and each TED was analysed using stem-and-leaf plots, box plots, as well as analysis of means (ANOM) and analysis of variance (ANOVA). The results obtained indicate the suitability of all proposed experimental designs for machinability studies.

Highlights

Turning is a type of material processing operation where a cutting tool is used to remove unwanted material to produce a desired product and it is generally performed on either conventional or computer numerically controlled (CNC) lathes
This study evaluates the full factorial design (FFD) and the Taguchi experimental design (TED) during longitudinal turning of tool steel material
The experimental data were extracted from a previous investigation regarding the evaluation of machinability in turning of AISI D6 tool steel, applying a feed forward back propagation (FFBP) artificial neural network (ANN); see [7]

Summary

Introduction

Turning is a type of material processing operation where a cutting tool is used to remove unwanted material to produce a desired product and it is generally performed on either conventional or computer numerically controlled (CNC) lathes. ANNs have been extensively applied in modeling many metal-cutting operations either conventional (turning, milling, grinding) or unconventional (EDM, AWJM, etc) [3-7]. This study evaluates the full factorial design (FFD) and the Taguchi experimental design (TED) during longitudinal turning of tool steel material. In this first attempt only cutting force is evaluated. The experimental data were extracted from a previous investigation regarding the evaluation of machinability in turning of AISI D6 tool steel, applying a feed forward back propagation (FFBP) artificial neural network (ANN); see [7].

Results and Discussion

Full Factorial Array

Conclusions