Journal of Production Engineering

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Vol. 27 No. 2 (2024)
Review Article

Impact of cutting parameters on surface roughness in aluminum alloys machining: a review of machine learning models for key parameter identification

FULL TEXT ARTICLE (PDF)
  • Dejan Bajić,
  • Mića Đurđev,
  • Slavica Prvulović,
  • Sanja Stanisavljev,
  • Dragan Ćoćkalo,
  • Luka Đorđević
Dejan Bajić
Technical faculty "Mihajlo Pupin"
Mića Đurđev
Technical faculty "Mihajlo Pupin", Zrenjanin
Slavica Prvulović
Technical faculty "Mihajlo Pupin", Zrenjanin
Sanja Stanisavljev
Technical faculty "Mihajlo Pupin", Zrenjanin
Dragan Ćoćkalo
Technical Faculty "Mihajlo Pupin", University of Novi Sad, Serbia
Luka Đorđević
Technical faculty "Mihajlo Pupin", Zrenjanin
DOI: https://doi.org/10.24867/JPE-2024-02-029

Published 2024-12-20

abstract views: 81 // FULL TEXT ARTICLE (PDF): 0


Keywords

  • Machine learning,
  • Surface roughness,
  • Regression

How to Cite

Bajić, Dejan, Mića Đurđev, Slavica Prvulović, Sanja Stanisavljev, Dragan Ćoćkalo, and Luka Đorđević. 2024. “Impact of Cutting Parameters on Surface Roughness in Aluminum Alloys Machining: A Review of Machine Learning Models for Key Parameter Identification”. Journal of Production Engineering 27 (2):29-37. https://doi.org/10.24867/JPE-2024-02-029.

Copyright (c) 2024 Journal of Production Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper provides a comprehensive review of research on the influence of machining parameters—depth of cut, feed rate, and cutting speed—on surface roughness, with a focus on aluminum alloys. Surface quality in CNC machining is significantly affected by these parameters, with numerous studies highlighting their impact on achieving desired surface roughness. The review analyzes findings from ten studies, emphasizing that feed rate is generally identified as the most influential parameter for surface roughness. While feed rate shows a dominant effect, cutting speed and depth of cut also contribute, though to a lesser extent. The research includes a discussion of various methodologies, including ANOVA, the Taguchi method, and more simple machine learning regression models, which demonstrate strong alignment with experimental results and highlight the effectiveness of advanced regression-based models in predicting surface roughness. The study concludes that optimizing feed rate is crucial for achieving high surface quality values, while cutting speed and depth of cut should be managed appropriately. The findings underscore the importance of machine learning tools in analyzing and optimizing machining parameters, offering practical guidance for CNC machine operators and engineers.

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