Analysis of 3D surface roughness in trochoidal milling of AA 6082 aluminium alloy

This research examines the effect of trochoidal milling strategy parameters, namely cutting speed (Vc), feed per tooth (fz) and trochoidal step (Ptr) on arithmetical mean height (Sa), maximum height (Sz) and the functional volume parameters, namely peak material volume (Vmp) and core material volume (Vmc). As a working material for trochoidal milling, the AA 6082 aluminium alloy was selected. By assigning three levels for machining parameters, an L9 Taguchi orthogonal array was adopted to design and conduct the experiments. The effects of trochoidal milling parameters on the responses were examined through analysis of variance (ANOVA), contour plots and 3D topographic analysis maps. Reliable regression models were generated to correlate the independent variables with the surface quality responses. The results revealed that significant differences are indicated regarding the hierarchy effect of trochoidal milling parameters on surface and functional volume indicators. For arithmetic mean height Sa, feed per tooth is the dominant parameter, followed by trochoidal step and cutting speed. Maximum height Sz exhibits a different hierarchy in terms of the main effects of cutting parameters. Cutting speed has the most significant effect followed by feed per tooth and trochoidal step. The dominant parameter for peak material volume Vmp is feed per tooth, followed by trochoidal step and cutting speed. In the case of core material volume Vmc, the feed per tooth is the most significant milling parameter, followed by the effects of trochoidal step and cutting speed.

Authors

Publication Details

Journal
Tribology and Materials
Published
2025-01-01
DOI
https://doi.org/10.46793/tribomat.2025.004
Citations
3
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
2.14
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of 3D surface roughness in trochoidal milling of AA 6082 aluminium alloy

Nikolaos M. Vaxevanidis, Rafał Kudelski, Nikolaos A. Fountas
3 citations
Tribology and Materials
Advanced machining processes and optimization
2.14
article

Analysis of 3D surface roughness in trochoidal milling of AA 6082 aluminium alloy

Nikolaos M. Vaxevanidis, Rafał Kudelski, Nikolaos A. Fountas
article en
3 citations

Abstract

This research examines the effect of trochoidal milling strategy parameters, namely cutting speed (Vc), feed per tooth (fz) and trochoidal step (Ptr) on arithmetical mean height (Sa), maximum height (Sz) and the functional volume parameters, namely peak material volume (Vmp) and core material volume (Vmc). As a working material for trochoidal milling, the AA 6082 aluminium alloy was selected. By assigning three levels for machining parameters, an L9 Taguchi orthogonal array was adopted to design and conduct the experiments. The effects of trochoidal milling parameters on the responses were examined through analysis of variance (ANOVA), contour plots and 3D topographic analysis maps. Reliable regression models were generated to correlate the independent variables with the surface quality responses. The results revealed that significant differences are indicated regarding the hierarchy effect of trochoidal milling parameters on surface and functional volume indicators. For arithmetic mean height Sa, feed per tooth is the dominant parameter, followed by trochoidal step and cutting speed. Maximum height Sz exhibits a different hierarchy in terms of the main effects of cutting parameters. Cutting speed has the most significant effect followed by feed per tooth and trochoidal step. The dominant parameter for peak material volume Vmp is feed per tooth, followed by trochoidal step and cutting speed. In the case of core material volume Vmc, the feed per tooth is the most significant milling parameter, followed by the effects of trochoidal step and cutting speed.

Tribology and MaterialsVol. 4(1)
Openalex Percentile: Top 15%
Advanced machining processes and optimization
2.14
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.