Milling Parameters for Surface Finish, Material Removal Rate, and Cutting Force Using Taguchi and Response Surface Methodology

This paper presents a systematic experimental investigation into the optimisation of CNC end milling process parameters for EN31 alloy steel using the Taguchi L9 orthogonal array design, Analysis of Variance (ANOVA), and Response Surface Methodology (RSM). Three machining strategies — conventional, climb, and high-speed milling — were evaluated at three levels of spindle speed (2000, 3000, 4000 rpm), feed rate (100, 150, 200 mm/min), and axial depth of cut (0.4, 0.8, 1.2 mm) using a TiAlN-coated solid carbide four-flute end mill. Response variables measured include surface roughness (Ra), material removal rate (MRR), and main cutting force (Fc). Signal-to-Noise (S/N) ratio analysis identified the optimal parameter combination as spindle speed 4000 rpm, feed rate 100 mm/min, axial depth of cut 0.8 mm, using climb milling strategy. ANOVA revealed spindle speed as the most significant factor with 43.1 percent contribution to surface roughness, followed by feed rate (28.6 percent) and milling strategy (16.2 percent). RSM second-order models predicted all three responses with R² greater than 0.96. Climb milling reduced surface roughness by 39.4 percent and cutting force by 21.8 percent compared to conventional milling. Confirmation experiments validated predicted optimal values with errors below 4.8 percent.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-16
DOI
https://doi.org/10.5281/zenodo.19603308
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Milling Parameters for Surface Finish, Material Removal Rate, and Cutting Force Using Taguchi and Response Surface Methodology

Vanduskar Vishakha Dadaso, Kalekar S. R.
Zenodo (CERN European Organization for Nuclear Research)
Advanced machining processes and optimization
article

Milling Parameters for Surface Finish, Material Removal Rate, and Cutting Force Using Taguchi and Response Surface Methodology

Vanduskar Vishakha Dadaso, Kalekar S. R.
article en

Abstract

This paper presents a systematic experimental investigation into the optimisation of CNC end milling process parameters for EN31 alloy steel using the Taguchi L9 orthogonal array design, Analysis of Variance (ANOVA), and Response Surface Methodology (RSM). Three machining strategies — conventional, climb, and high-speed milling — were evaluated at three levels of spindle speed (2000, 3000, 4000 rpm), feed rate (100, 150, 200 mm/min), and axial depth of cut (0.4, 0.8, 1.2 mm) using a TiAlN-coated solid carbide four-flute end mill. Response variables measured include surface roughness (Ra), material removal rate (MRR), and main cutting force (Fc). Signal-to-Noise (S/N) ratio analysis identified the optimal parameter combination as spindle speed 4000 rpm, feed rate 100 mm/min, axial depth of cut 0.8 mm, using climb milling strategy. ANOVA revealed spindle speed as the most significant factor with 43.1 percent contribution to surface roughness, followed by feed rate (28.6 percent) and milling strategy (16.2 percent). RSM second-order models predicted all three responses with R² greater than 0.96. Climb milling reduced surface roughness by 39.4 percent and cutting force by 21.8 percent compared to conventional milling. Confirmation experiments validated predicted optimal values with errors below 4.8 percent.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 8%
Advanced machining processes and optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.