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.

This study presents a comprehensive evaluation of machinability in turning operations by reviewing and synthesizing the literature based on the parameters of cutting forces, tool wear, and surface roughness. Previous studies clearly present the significance of cutting parameters. While an increase in speed mainly reduces the forces and roughness, increases in feed and depth tend to increase cutting forces, temperature, and wear by enlarging the contact area. Tool wear directly affects the surface integrity and process stability through flank/crater wear and BUE formation. In particular, austenitic stainless steels, titanium and nickel-based superalloys, and particle-reinforced composites are sensitive to the cutting mechanism, tool-material interaction, and speed. Environmentally sustainable cooling-lubrication strategies (MQL, plant-based oils, cryogenic/CO₂-based solutions) reduce wear by reducing friction and temperature, improving the surface quality. In the case of ultrasonic turning, similar benefits are provided by reducing instantaneous contact and average forces. In the examined studies, dynamometers, scanning electron microscopes, surface roughness measurement instruments, and various software were used. Additionally, optimum parameter windows were reported based on Taguchi/ANOVA/RSM methods. Consequently, the combination of appropriate parameter selection and environmentally friendly cooling and lubrication and/or auxiliary processes extends tool life, improves surface integrity, and increases energy and cost efficiency. This approach offers a repeatable and industrially applicable roadmap, even for materials that are difficult to machine.

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.

In this study, the microstructural, mechanical, wear, and corrosion behavior of Ni-Al-Cr and Ni–Al–Cr/SiC composite coatings with different composition ratios, produced by electric arc melting on Inconel 600 substrates, was systematically investigated. Microhardness measurements revealed a significant and consistent increase in the hardness values of the coatings depending on the increase in SiC reinforcement ratio (1%, 3%, and 5%). Wear tests showed that the coated samples exhibited significantly higher wear resistance compared to the pure Inconel 600 substrate. A significant improvement in wear resistance was achieved with the addition of SiC at 1% and 3% weight percentages; the width and depth of wear tracks were significantly reduced with SiC reinforcement. In contrast, increasing the SiC ratio to 5% weight percentage led to a decrease in wear resistance. This was attributed to particle aggregation at high SiC content, weakening of bonds at the matrix-reinforcement interface, and the behavior of SiC particles separated from the matrix as third-body abrasives. Electrochemical corrosion tests have shown that SiC-reinforced coatings form a more stable and permanent passive film, and corrosion resistance increases as the SiC content increases (1%, 3%, and 5%). The results indicate that the SiC reinforcement ratio affects the mechanical and electrochemical properties of Ni-Al-Cr/SiC composite coatings produced by electric arc melting.

In this study, the machinability of pure zinc during dry turning is examined experimentally. Low melting point and high ductility of zinc enable easy machining, while low strength and hardness result in a limiting factor for the usage of high-loading applications. Zinc samples (99.58% purity) are turned using a TiAlNi-coated carbide cutting tool at feed rates of 0.1-0.2mm/rev, depths of cut of 0.2-0.4mm, and cutting speeds of 20-40m/min. Surface roughness was evaluated as the main indicator of machinability, and the effects of parameters were statistically examined using S/N and ANOVA analyses. Optimum surface quality was observed at a low feed rate (0.1mm/rev), low cutting speed (20m/min), and 0.2mm depth of cut. Maximum surface roughness was achieved at high cutting speed and feed rate settings. The ANOVA results revealed that the depth of cut was the most influential parameter on surface roughness, followed by the feed rate and cutting speed, respectively. The R² value of 91.32% indicates a strong correlation between the model and the experimental results.

Machining is essentially a process of creating and breaking chips, during which it is desired to clean the surface of the workpiece and create a new surface to obtain a smoother and mechanically long-lasting structure. Machining involves many processes; each specialized for a specific purpose. One of the most important machining processes is turning. Turning is performed using a variety of machine tools with different machining parameters, cutting tools, and fixtures. This paper focuses on the machinability study of AA7075-T6 alloy considering the factors such as cutting speed, feed rate and coating materials on the cutting tool. The response parameters evaluated in this study is the cutting forces, cutting temperatures, energy consumption. According to the findings, feed rate, cutting speed and coating materials were found as the dominant parameters on cutting forces, energy consumption and cutting temperatures respectively. This situation is seen in the ANOVA tables that feed is the most effective parameter for cutting force with 33.05%, coating is the most effective parameter for temperature with 3.45% and cutting speed is the most effective parameter for energy consumption with 45.54%. The frame developed for this paper aims to contribute to the current literature by comparing modern coating materials considering popular and valid machinability indicators.