## 1 Introduction  
Environmental issues are linked to modern technology, especially with the need for a significant amount of energy and cost in the manufacturing processes of individual products and the tools used to fabricate them. The tool costs cover a significant part of the total outcome of the machining process. Reduction in these costs is achieved mainly by improving the tools and their geometry [1; 2; 3], tool materials [1; 2; 3], lubricating the edge, and optimizing the cutting parameters using artificial intelligence [4; 5; 6], among others. The techniques used for removing material from the workpiece have a wide span and involve numerous mechanisms/styles with which the cutting tool and workpiece interact. The quality of the machined parts is based on the performance of three elements, such as machine tools, workpiece materials, and cutting tools, as they play a critical role in the capability and performance. Additionally, standard input parameters such as speed, feed, and depth of cut set by experts are equally significant in reducing inconsistency in machining. However, anomalous moments evolved during machining due to the material-based variations, failure of rotating machine elements assisting machining, etc., which are difficult to avoid. In addition, the effect of temperature and pressure on material properties makes machining more complex. This affects overall machining, including cutting tool edges, the surface finish of the workpiece, and ultimately the cost involved. Moreover, owing to their peculiar nature, they are challenging to monitor and forecast. Let it be any operation, such as grinding, milling, broaching, turning, boring, and turning, knowing material properties, machining mechanisms, and technological situations stands inevitable to improve overall performance. Examining this subject from the perspective of diversified modern sectors, such as automotive, space, aeronautics, medicine, medical, etc., the parts manufactured for these areas require excellent surface qualities. To obtain such properties, it is essential to create ideal cutting conditions in terms of the geometry and material of the cutting tool, cooling and lubricating mediums, correctly selected cutting parameters, etc. All these contributors affect the cutting performance slightly or greatly depending on the material properties and type of the process, namely roughing, finishing, precision leveling, etc. In this complex machining technology, tool wear is a critical factor due to its correlation with surface-related characteristics such as roughness, topography, waviness, residual stress, etc. The definition of tool life addresses the helpful utilization of a cutting tool. The conserving the health of the cutting tool is of great importance, not only to benefit from it in maximum time, but also to protect the machined surface from detrimental impacts. Therefore, researchers have repeatedly focused on models to improve the life of the tool. Using a neural network approach, [PERSON] et al. [7] investigated life prediction and modeling of a carbide tool during turning of composite material. They stated that the results of the tool-life model calculated with the artificial neural network are very close to the physical experiment results and that the artificial neural network model predicts the experimental results with minimum error. [PERSON] et al. [8] have analyzed the machinability of composite materials using the neural network approach. [PERSON] et al. [9] studied surface roughness of AISI 5140, modeling cutting parameters and tool geometry by means of response surface methodology for multicriteria optimization of vibration generated during machining. [PERSON] et al. [10] investigated the optimum cutting parameters required for turning Ti6 Al4V material. Their study was carried out in dry cutting conditions using uncoated H13 carbide inserts according to wear rate, specific cutting energy, material removal rate, and roughness.