Impact of cutting parameters on surface roughness in aluminum alloys machining: a review of machine learning models for key parameter identification - Bajić, Dejan; Đurđev, Mića; Prvulović, Slavica; Stanisavljev, Sanja; Ćoćkalo, Dragan; Đorđević, Luka (Journal of Production Engineering, 2024)
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.