Finish turning of toolox 33 to improve machining parameters with different nose radius tools

Turning, the most widely used machining process in manufacturing, continues to maintain its popularity today. Given its ongoing relevance, evaluating machinability in turning operations remains critical. In this study, dry turning was applied to Toolox 33, a material commonly used due to its favorable machinability characteristics. In the experimental research, changes in surface roughness and cutting force (two of the most critical output parameters) were evaluated in the context of machinability by applying different values of machining parameters, including tool nose radius, cutting speed, feed, and cutting depth. The investigation was undertaken with consideration of integrating machine learning methods into the manufacturing process. The results of the study indicated that optimal cutting force values can be achieved by employing a larger tool nose radius, higher cutting speeds, and lower feed rates and depths of cut. Similarly, optimal surface roughness was obtained under conditions involving a larger nose radius tool, lower feed, and shallower cutting depth. However, variations in the cutting speed parameter led to differing results in surface roughness. For instance, while an increase in cutting speed led to lower surface roughness values in some experimental sets, an increase in surface roughness was observed in others. Graphical evaluations confirmed the suitability of machine learning techniques for this application. The optimum cutting force was recorded under experimental conditions involving a 0.8 mm nose radius tool, a feed rate of 0.2 mm/rev, a depth of cut of 0.2 mm, and a cutting speed of 60 m/min. The best surface roughness results were obtained in the same experiment that yielded the optimum cutting force values. Compared to the optimum result obtained with a 0.8 mm nose radius tool, reducing the nose radius to 0.4 mm increased the cutting force by 29.87%, increasing the feed rate to 0.4 mm/rev led to a 100% rise, and increasing the depth of cut to 0.4 mm resulted in a 62.33% increase. In contrast, increasing the cutting speed from 40 m/min to 60 m/min reduced the cutting force by 44.20%. Following the physical experiments, it was observed that increasing the cutting speed from 40 to 60 m/min reduced surface roughness (Ra) by approximately 5% to 22%, while increasing the cutting depth from 0.2 mm to 0.4 mm and the feed rate from 0.2 mm/rev to 0.4 mm/rev led to increases of 65.28% and 147.93% in Ra, respectively. Additionally, compared to the 0.4 mm nose radius tool, the use of a 0.8 mm nose radius tool, which yielded the optimum surface quality, resulted in a 34.80% improvement in surface roughness.

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Publication Details

Journal
European Mechanical Science
Published
2025-09-19
DOI
https://doi.org/10.26701/ems.1724270
Citations
5
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
3.57
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article

Finish turning of toolox 33 to improve machining parameters with different nose radius tools

Tayfun Çetin, Rüstem Binali, Kübra Kaya, Hakan Gündoğmuş
5 citations
European Mechanical Science
Advanced machining processes and optimization
3.57
article

Finish turning of toolox 33 to improve machining parameters with different nose radius tools

Tayfun Çetin, Rüstem Binali, Kübra Kaya, Hakan Gündoğmuş
article en
5 citations

Abstract

Turning, the most widely used machining process in manufacturing, continues to maintain its popularity today. Given its ongoing relevance, evaluating machinability in turning operations remains critical. In this study, dry turning was applied to Toolox 33, a material commonly used due to its favorable machinability characteristics. In the experimental research, changes in surface roughness and cutting force (two of the most critical output parameters) were evaluated in the context of machinability by applying different values of machining parameters, including tool nose radius, cutting speed, feed, and cutting depth. The investigation was undertaken with consideration of integrating machine learning methods into the manufacturing process. The results of the study indicated that optimal cutting force values can be achieved by employing a larger tool nose radius, higher cutting speeds, and lower feed rates and depths of cut. Similarly, optimal surface roughness was obtained under conditions involving a larger nose radius tool, lower feed, and shallower cutting depth. However, variations in the cutting speed parameter led to differing results in surface roughness. For instance, while an increase in cutting speed led to lower surface roughness values in some experimental sets, an increase in surface roughness was observed in others. Graphical evaluations confirmed the suitability of machine learning techniques for this application. The optimum cutting force was recorded under experimental conditions involving a 0.8 mm nose radius tool, a feed rate of 0.2 mm/rev, a depth of cut of 0.2 mm, and a cutting speed of 60 m/min. The best surface roughness results were obtained in the same experiment that yielded the optimum cutting force values. Compared to the optimum result obtained with a 0.8 mm nose radius tool, reducing the nose radius to 0.4 mm increased the cutting force by 29.87%, increasing the feed rate to 0.4 mm/rev led to a 100% rise, and increasing the depth of cut to 0.4 mm resulted in a 62.33% increase. In contrast, increasing the cutting speed from 40 m/min to 60 m/min reduced the cutting force by 44.20%. Following the physical experiments, it was observed that increasing the cutting speed from 40 to 60 m/min reduced surface roughness (Ra) by approximately 5% to 22%, while increasing the cutting depth from 0.2 mm to 0.4 mm and the feed rate from 0.2 mm/rev to 0.4 mm/rev led to increases of 65.28% and 147.93% in Ra, respectively. Additionally, compared to the 0.4 mm nose radius tool, the use of a 0.8 mm nose radius tool, which yielded the optimum surface quality, resulted in a 34.80% improvement in surface roughness.

European Mechanical ScienceVol. 9(3)
Selçuk University (TR), Hakkari University (TR)
Openalex Percentile: Top 7%
Advanced machining processes and optimization
3.57
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