###### Abstract
[PERSON] (2016) proposed mathematical models for the components that comprise cutting force, the power consumed in cutting, and workpiece temperature in high-speed turning, demonstrating the advantages of high-speeds over conventional turning speeds. [PERSON] et al. [PERSON] et al. (2016) developed a thermal imaging method to select the optimal cutting conditions for high-temperature intermetallic alloys used in the aviation industry. [PERSON] et al. [PERSON] et al. (2016) presented a detailed review focusing on the power issue and tool life expectancy with the help of statistical approaches in manufacturing specified products. These studies have clearly demonstrated that the influence of cutting speed is more dominant on selected responses. [PERSON] et al. [PERSON] et al. (2016) presented a novel means of estimating the effect of feed rate and cutting speed on the rate of tool wear, as related to changes in the three force components in the turning of a difficult-to-cut medical grade Co-Cr-Mo (ASTM F75) alloy. [PERSON] et al. [PERSON] et al. (2016) evaluated the performance of different work-tool combinations. Variations in the materials and tool grades were common. The turning tests were performed to evaluate the tool performance criteria, and eventually the found results were satisfactory. [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) used seamless tubular workpieces to define surface roughness and tool wear while turning using a cold/dry air system. The results demonstrated significant reductions in tool life at high cutting speeds. Increased cutting speeds also reduced the cutting effort, both in dry and cold air cutting conditions. [PERSON] et al. [PERSON] et al. (2016) used infrared thermographic monitoring to determine the impact of feed, cutting speed, and depth on machine tool vibrations, tool tip/chip temperatures and surface roughness. With active control of the speed of the main drive, the dynamic state often associated with machine-tool vibrations can be avoided. Many researchers have also studied the impact of cutting speed on surface roughness, cutting forces, and tool life in turning operations using experimental and artificial intelligence methods. [PERSON] and [PERSON] [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) predicted the surface quality parameter by the adoption of different neural network algorithms. The surface roughness (\(R_{a}\)) was modeled in terms of cutting speed, feed rate and cooling conditions. [PERSON] and [PERSON] [16] used regression analysis for modeling surface roughness in dry single-point turning and for its optimization. They demonstrated the decreasing tendency of surface roughness at increasing cutting speeds and decreasing feed rate and cutting depth. Using an artificial intelligence approach and statistical method, [PERSON] and [PERSON] [17] developed prediction and optimization models for the average surface roughness parameter. The behavior of the surface roughness parameter was justified by analytical relationships. [PERSON] et al. [18] estimated the effects of cutting parameters (speed, feed, nose radius) on workpiece vibration, machined surface roughness, and the volume of metal removed in steel boring operations (AISI1040). [PERSON] and [PERSON] [19] optimized the cutting speeds, feed rates and depth of cut values for minimizing the surface roughness during the turning of Ni-hard materials with ceramic and cubic boron nitride (CBN) tools. [PERSON] [20] employed a machine learning approach to solve the optimization problem for surface roughness in turning hardened steel under sustainable cooling condition. In a study by [PERSON] et al. [21], tool wear was monitored when employing various insert shapes and geometries, using low policrystaline cubic boron nitride (PCBN) inserts and tool-edge preparation, tool coatings, fluid pressure, cutting speeds, and feed rates in the finish turning of Inconel 718. [PERSON] et al.