The aim of this study is to evaluate the effect of various lubrication systems (dry cutting, MQL, and nano-MQL) on the machinability of AISI 1040 medium-carbon steel. By dispersing titanium carbide (TiC) nanoparticles into environmentally friendly sunflower oil, a new type of nano-MQL fluid was developed. Machinability parameters such as surface finish, cutting force, energy consumption, chip structure, and tool degradation were examined through scanning electron microscopy (SEM). Based on experimental observations, the use of the nano-MQL technique led to a notable enhancement in machining performance when compared to both dry and traditional MQL machining. In addition, surface roughness was substantially reduced with the nano-MQL, suggesting more effective lubrication and cooling. Reductions in cutting forces and energy consumption were also observed, indicating more efficient material removal and lower mechanical resistance. The SEM examination of the cutting tools proved the low wear rate of the nano-MQL, which exhibited less adhesion and more abrasion wear, and of dry cutting, which showed the most serious wear. Furthermore, chip morphology illustrations indicated that the chips of nano-MQL were relatively uniform and segmented, indicating superior chip breaking quality and cutting stability. The results suggest that employing TiC nanoparticles in MQL offers a clear enhancement of cutting performance in terms of process efficiency, surface quality, and tool wear. These results validate the capability of nano-MQL as an environmentally friendly and high-performance lubrication method for turning medium-carbon steels, supporting more sustainable and efficient manufacturing operations.

AISI 52100 malzemesi esas olarak rulman endüstrisinde kullanılmaktadır. Bu çelikler yüksek sertlik, mükemmel aşınma direnci ve boyutsal stabilite sunar. Daha yüksek mukavemet olması, özellikle otomotiv endüstrisinde rulman uygulamaları için uygun hale getirmektedir. Bu çalışmada AISI 52100 çeliğinin işlenebilirlik özellikleri iki farklı kesme hızı, ilerleme hızı ve talaş derinliğine göre kuru kesme koşullarında tornalama işlemine göre değerlendirilmiştir. İşlenebilirlik çıktı parametreleri ise kesme kuvveti, takım ucu sıcaklığı ve yüzey pürüzlülüğünü içermektedir. İşlenebilirlik deneyleri tam faktöriyel deneysel tasarım olarak gerçekleştirilmiştir. Çalışmada TiC kaplı kesici takım kullanılmıştır. Çalışma sonucunda, yüksek kesme hızlarında rulman çeliğinin işlenmesinde yüzey pürüzlülük değerlerinde azalma tespit edilmiştir. Ayrıca, düşük ilerleme oranında işleme koşullarında kesme kuvveti değeri azalmıştır. Bu da daha düşük güç tüketimi ile işleme verimliliğini arttırmaktadır.. Yüzey pürüzlülüğü üzerinde en etkili faktörün ilerleme miktarı olduğu, ardından talaş derinliğinin olduğu görülmüştür. Sıcaklık değerinin artmasında ise en etkili parametrenin talaş derinliği olduğu sonucuna varılmıştır.

The main challenges in turning are the quality of the machined part and the cost of tooling. Therefore, optimum machining parameters suitable for turning operations should be selected to achieve the desired quality of the finished product with reduced machining time and cost. The aim of this study is to determine the optimum machining conditions for S235JR low carbon steel without heat treatment, which could include finding the right combination of cutting speed, feed rate, depth of cut and tool material to achieve efficient material removal and desired surface finish. The experimental study, designed with the full factorial method, was carried out with 2 factors of cutting speed and feed rate with selected 2 levels under dry and MQL cutting environment conditions. Results of this study showed that mist lubricating technique overcome the machinability challenges of S235JR steel in terms of low surface quality and high cutting temperature and cutting force.

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.

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.