Akademik Veri Yönetim Sistemi
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, cilt.46, sa.8, 2024 (SCI-Expanded)
Advanced tool designs are essential for harnessing the full potential of end milling techniques, which have long served as a cornerstone of the industry. Due to the unique difficulties of designing end milling tools, where many parameters interact in complex ways, it is important to be aware of the limits of relying only on experiments and human judgment to find the best cutting tool geometry. Consequently, advanced data analytics techniques, computational analyses, and optimization strategies play a critical role in this process. Currently, there is a lack of comprehensive studies that thoroughly investigate the impact of end mill geometry on milling 316L stainless steel, considering eight parameters at three different levels and their effects on resultant cutting forces. To address this gap, this research adopts a holistic approach by integrating finite element analysis (FEA), response surface methodology (RSM), and analysis of variance (ANOVA) to develop a predictive model that evaluates the effects of geometric parameters on the resultant cutting forces. The findings indicate that the radial relief angle significantly influences the resultant cutting force, marking it the most critical design parameter. The model effectively predicts cutting forces with a reasonable degree of accuracy, as evidenced by a R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>{2}$$\end{document} value of 83.96% and an adjusted R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>{2}$$\end{document} value of 69.92%. Notably, the resultant cutting force, optimized to 288.73 N, showed a substantial decrease -approximately threefold compared to preliminary experimental results- highlighting the effectiveness of our model and approach.