An Investigation of Machinability of Hot Work Tool Steel Toolox 44 with Cutting Tools with Different Nose Radius Using Machine Learning

Authors : Kübra Kaya, Tayfun Çetin, Rüstem Binali, Hakan Gündoğmuş

Pages : 164-183

Doi:10.52795/mateca.1644170

View : 73 | Download : 81

Publication Date : 2025-08-30

Article Type : Research Paper

Abstract :The advancement of technology has provided a new perspective for the manufacturing industry, accelerating research on machinability studies. The evaluation of key output parameters such as cutting force and temperature, surface roughness concerning input parameters (cutting speed, feed, depth of cut) is among the most common and comprehensive research topics in this field. In this study, dry turning operations were performed on Toolox 44 tool steel using input parameters of two varied feed rates (0.17, 0.34 mm/rev), two dissimilar cutting depths (0.2 mm, 0.4 mm), two distinct cutting speeds (40, 60 m/min), two different cutting tool nose radius (0.4 mm, 0.8 mm). The resulting parameters, including cutting temperature, force and surface roughness, were evaluated using graphical analysis and machine learning methods, specifically the decision tree and heat map approaches. The study\\\'s findings indicated that as the feed coupled with cutting depth enhanced, the cutting force also increased, whereas higher cutting speeds led to a decrease in the cutting force. Additionally, the reduction in cutting tool nose radius exhibited varying trends depending on different parameter combinations. It was determined that cutting temperature increased with higher feed and cutting depth, while the variation in cutting speed resulted in different increasing or decreasing trends in cutting temperature. The data revealed that surface roughness went up with an augment in feed, while it lowered as the cutting speed was raised. Additionally, an increase in cutting depth reduced surface roughness in the experiment set with a smaller tool nose radius, while it increased surface roughness in the experiment set with a larger tool nose radius. The results of the graphical evaluation were compared with those of another assessment method, namely machine learning, and it was found that there is a consistent level of accuracy between the two approaches. In the experimental setup with a 0.8 mm tool nose radius, cutting force, cutting temperature, and surface roughness increased by 187.73%, 20.05%, and 181.23%, respectively. For the 0.4 mm radius, the respective increases were 325.60%, 20.55%, and 132.52%. These results suggest that the 0.8 mm tool nose radius offers better machinability performance.
Keywords : Toolox 44, İşlenebilirlik, Farklı Yarıçap, Takım Çeliği, Kesici Takımlar

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