## 1 Introduction
have considered the optimization of hard-turning parameters using evolutionary algorithms [19]. In their work, the investigated machining parameters were cutting speed, feed rate, and depth-of-cut, while the output parameters were surface roughness and cutting temperature. An example of a cutting temperature prediction for the turning of biomedical stainless steel is carried out by [PERSON] et al. [20]. These authors used a neural network with an optimization algorithm for the quick, easy, and successful optimization of input regimes. Consequently, an intelligent model on the basis of a neural network is a good feature for generalization and has the ability to accept non-linear variables with unknown iterations. [PERSON] et al. predicted tool life in turning operations using neural networks [21]. Their results confirm that the combination image recognition software and ANN (Artificial neural networks) modeling could potentially grow into a useful industrial tool for cheap estimation of tool life in agile operations. One example of the application of fuzzy logic is shown by [PERSON] et al. [22]. They used a fuzzy logic analysis method to predict the optimal solution, and to find out the most influential parameter to determine the output characteristics. Also, the use of artificial intelligence tools, such as an adaptive neural fuzzy inference system (ANFIS), enables a positive development of the manufacturing industry [23]. ANFIS is one of the most powerful techniques used for modelling, and is based on a fuzzy inference system and neural network, which are used to model complex relationships in various branches of industry and engineering that are difficult or impossible to explain to classical models [24]. In addition to prediction and optimization, which give a fuzzy logic, neural networks, and evolutionary algorithms, the ANFIS technique provides the ability to recognize the significance of input parameters. This intelligence technique has been successfully used in modelling various machining processes, such as turning, milling, and drilling [25; 26; 27]. According to literary sources, in predicting the output characteristics of the cutting process, ANFIS models have proven to be very efficient. Machining of hard materials by turning has been receiving special attention because it offers many possible benefits over grinding in machining hardened steel [28; 29]. Research shows that it is possible to perform machining on the turn while achieving a high accuracy and small roughness of the treated surface, in contrast with the more expensive grinding, which allows for significant technical and economic effects, the most important of which are:  
* Reducing the processing time and costs (in relation to finishing grinding). * It is feasible to handle multiple surfaces in one clamp, which is rarely possible when grinding, avoiding the effects that occur during grinding (the appearance of structural changes due to overheating of the surface layer, residual stresses, and cracks), and improving the exploitation characteristics of the parts. The use of cubic boron nitride (CBN) tools for rigid turning for rough- and semi-finishing is recognized as a technologically and economically flexible process. Because of the poor thermal conductivity of CBN, the heat generated during the cutting process is taken with a part separated from the cutting zone, eliminating the use of coolants and lubricants. In addition, the dry surface finish is most effective in the realization of the process due to more stringent regulations on the protection of the environment and increasing the costs associated with disposing substances used for cooling and lubrication. The use of coolants/lubricants during cutting is widely accepted as a temperature control for improving the quality of the system and surface. However, the practice of using coolants/lubricants is expensive and causes serious damage to the environment and human health [19]. Processing of hard materials requires the use of special and expensive tools.